Presipitation analysis plays an important role in hydrological studies. In this study, using 50 years of rainfall data and ARIMA model, critical areas of Iran were determined. For this purpose, annual rainfall data of 112 different synoptic stations in Iran were gathered. To summarize, it could be concluded that: ARIMA model was an appropriate tool to forecast annual rainfall. According to obtained results from relative error, five stations were in critical condition. At 45 stations accrued rainfalls with amounts of less than half of average in the 50-year period. Therefore, in these 45 areas, chance of drought is more than other areas of Iran.
Application of mathematical modelling in rainfall forcast a csae study in...eSAT Journals
Abstract Malaysia receives rainfall from 2000 mm to 4000 mm annually where it is greatly influenced by two monsoon periods in November to March and May to September. The state of Sarawak is well known for its long and wide rivers. Numerous activities such as commercial, industrial and residential can always be found in the vicinity of the rivers. The activities have started since decades ago and still continue to grow and spatially expanding through times providing incomes ranging from small farmers to the largest corporations. Unfortunately, these areas are expected to experience frequent flood events as well as possible receding water level in rivers based on the findings of previous studies. If the projections are accurate, the productivity of these activities will be reduced, hence, in a longer term may affect the economy of the state as whole as well. Therefore, there is an urgent need for existing knowledge on rainfall behavior to be revised as effects of climate change with the intention that the state can fully utilize the favorable conditions and make scientific based decisions in the future. Recent study reveals that the Fourier series (FS), has the ability to simulate long-term rainfall up to 300 years is viewed as an important finding in the study of rainfall forecast. Long-term rainfall forecasting is viewed to be beneficial to the state of Sarawak in its future planning in various sectors such as water supply, flood mitigation, river transportation as well as agriculture. The main goal of the study is to apply a mathematical modeling in rainfall forecasting for the Sungai Sarawak basin. Data from eight rain gauge stations was analyzed and prepared for missing data, consistency check and adequacy of number of stations. Simple statistical analysis was conducted on the data such as maximum, minimum, mean and standard deviation. 27 years of annual rainfall data were simulated with the Fourier Series equation using spreadsheet. Hence, the result was compared with the Fitting N-term Harmonic Series. The model result reveals that the Fourier Series has the ability to simulate the observed data by being able to describe the rainfall pattern and there is a reasonable relationship between the simulation and observed data with p-value of 0.93. Keywords: Fourier series, Mathematical
Runoff Prediction of Gharni River Catchment of Maharashtra by Regressional An...ijtsrd
The present study deals with the prediction of runoff of a river catchment of maharastra by using linear regressional analysis and self organizing maps by handling numerical data. The prediction is done by using past data record. A mathematical model has been developed for rainfall runoff correlation. Warish Khan | Adil Masood | Najib Hasan"Runoff Prediction of Gharni River Catchment of Maharashtra by Regressional Analysis and Ann Tool Box" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7025.pdf http://www.ijtsrd.com/engineering/civil-engineering/7025/runoff-prediction-of-gharni-river-catchment-of-maharashtra-by-regressional-analysis-and-ann-tool-box/warish-khan
A comparative study of different imputation methods for daily rainfall data i...journalBEEI
Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.
An Attempt To Use Interpolation to Predict Rainfall Intensities tor Crash Ana...IJMERJOURNAL
ABSTRACT: This study uses different interpolation techniques to predict rainfall intensity at locationsthat are not directly located near a rainfall gauges. The goal of being able to interpolate the rainfall intensity is to study its impact on traffic crashes. To perform the study, a collection of rainfall gauges in Alabama were used as subject locations where rainfall intensity was predicted from surrounding gauges, while also providing validation data to compare the predictions. Essentially, the actual rainfall intensities at existing gauges were interpolated using nearby gauges and the results were analyzed.The interpolation techniques used in the study included proximal, averaging and a distance weighted average. The results of the study indicated that none of the interpolation methodologies were sufficient to accurately predict the rainfall intensity values any significant distance from the actual gauges.
Time Series Data Analysis for Forecasting – A Literature ReviewIJMER
In today's world there is ample opportunity to clout the numerous sources of time series data
available for decision making. This time ordered data can be used to improve decision making if the data
is converted to information and then into knowledge which is called knowledge discovery. Data Mining
(DM) methods are being increasingly used in prediction with time series data, in addition to traditional
statistical approaches. This paper presents a literature review of the use of DM and statistical approaches
with time series data, focusing on weather prediction. This is an area that has been attracting a great deal
of attention from researchers in the field.
Estimation of Annual Runoff in Indravati Sub Basin of Godavari River using St...AM Publications
Prediction of runoff from known rainfall is one of the major problems confronted by hydrologists. There is lack of availability of long period runoff records in large number of catchments in India. Investigators have proposed many empirical relationships for runoff estimation in different catchments based on limited data of parameters affecting runoff. These regional relationships are useful in planning of water resource projects. This study was carried out to obtain simple yet effective relationship for estimation of annual runoff in Indravati sub basin of Godavari river. Regression analysis was carried out using annual rainfall, annual runoff and average annual temperature data to develop empirical models for annual runoff estimation. GIS software was used for preparing maps for the study area and to extract the precipitation and temperature data available in grid format from IMD. The best suited empirical model is then selected as per statistical criteria with lower values of standard error, standard deviation, mean absolute deviation (MAD), root mean square error (RMSE) and higher values of R square and correlation coefficient. Statistical significance of selected empirical model was evaluated by paired t test, F test and P value at 95 % confidence level. The developed relationship is then compared with the existing Khosla and Inglis and DeSouza relationships. Outcome of this comparison produces encouraging inferences to suggest an effective regional relationship for annual runoff estimation in the Indravati sub basin of Godavari river in India.
Application of mathematical modelling in rainfall forcast a csae study in...eSAT Journals
Abstract Malaysia receives rainfall from 2000 mm to 4000 mm annually where it is greatly influenced by two monsoon periods in November to March and May to September. The state of Sarawak is well known for its long and wide rivers. Numerous activities such as commercial, industrial and residential can always be found in the vicinity of the rivers. The activities have started since decades ago and still continue to grow and spatially expanding through times providing incomes ranging from small farmers to the largest corporations. Unfortunately, these areas are expected to experience frequent flood events as well as possible receding water level in rivers based on the findings of previous studies. If the projections are accurate, the productivity of these activities will be reduced, hence, in a longer term may affect the economy of the state as whole as well. Therefore, there is an urgent need for existing knowledge on rainfall behavior to be revised as effects of climate change with the intention that the state can fully utilize the favorable conditions and make scientific based decisions in the future. Recent study reveals that the Fourier series (FS), has the ability to simulate long-term rainfall up to 300 years is viewed as an important finding in the study of rainfall forecast. Long-term rainfall forecasting is viewed to be beneficial to the state of Sarawak in its future planning in various sectors such as water supply, flood mitigation, river transportation as well as agriculture. The main goal of the study is to apply a mathematical modeling in rainfall forecasting for the Sungai Sarawak basin. Data from eight rain gauge stations was analyzed and prepared for missing data, consistency check and adequacy of number of stations. Simple statistical analysis was conducted on the data such as maximum, minimum, mean and standard deviation. 27 years of annual rainfall data were simulated with the Fourier Series equation using spreadsheet. Hence, the result was compared with the Fitting N-term Harmonic Series. The model result reveals that the Fourier Series has the ability to simulate the observed data by being able to describe the rainfall pattern and there is a reasonable relationship between the simulation and observed data with p-value of 0.93. Keywords: Fourier series, Mathematical
Runoff Prediction of Gharni River Catchment of Maharashtra by Regressional An...ijtsrd
The present study deals with the prediction of runoff of a river catchment of maharastra by using linear regressional analysis and self organizing maps by handling numerical data. The prediction is done by using past data record. A mathematical model has been developed for rainfall runoff correlation. Warish Khan | Adil Masood | Najib Hasan"Runoff Prediction of Gharni River Catchment of Maharashtra by Regressional Analysis and Ann Tool Box" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7025.pdf http://www.ijtsrd.com/engineering/civil-engineering/7025/runoff-prediction-of-gharni-river-catchment-of-maharashtra-by-regressional-analysis-and-ann-tool-box/warish-khan
A comparative study of different imputation methods for daily rainfall data i...journalBEEI
Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.
An Attempt To Use Interpolation to Predict Rainfall Intensities tor Crash Ana...IJMERJOURNAL
ABSTRACT: This study uses different interpolation techniques to predict rainfall intensity at locationsthat are not directly located near a rainfall gauges. The goal of being able to interpolate the rainfall intensity is to study its impact on traffic crashes. To perform the study, a collection of rainfall gauges in Alabama were used as subject locations where rainfall intensity was predicted from surrounding gauges, while also providing validation data to compare the predictions. Essentially, the actual rainfall intensities at existing gauges were interpolated using nearby gauges and the results were analyzed.The interpolation techniques used in the study included proximal, averaging and a distance weighted average. The results of the study indicated that none of the interpolation methodologies were sufficient to accurately predict the rainfall intensity values any significant distance from the actual gauges.
Time Series Data Analysis for Forecasting – A Literature ReviewIJMER
In today's world there is ample opportunity to clout the numerous sources of time series data
available for decision making. This time ordered data can be used to improve decision making if the data
is converted to information and then into knowledge which is called knowledge discovery. Data Mining
(DM) methods are being increasingly used in prediction with time series data, in addition to traditional
statistical approaches. This paper presents a literature review of the use of DM and statistical approaches
with time series data, focusing on weather prediction. This is an area that has been attracting a great deal
of attention from researchers in the field.
Estimation of Annual Runoff in Indravati Sub Basin of Godavari River using St...AM Publications
Prediction of runoff from known rainfall is one of the major problems confronted by hydrologists. There is lack of availability of long period runoff records in large number of catchments in India. Investigators have proposed many empirical relationships for runoff estimation in different catchments based on limited data of parameters affecting runoff. These regional relationships are useful in planning of water resource projects. This study was carried out to obtain simple yet effective relationship for estimation of annual runoff in Indravati sub basin of Godavari river. Regression analysis was carried out using annual rainfall, annual runoff and average annual temperature data to develop empirical models for annual runoff estimation. GIS software was used for preparing maps for the study area and to extract the precipitation and temperature data available in grid format from IMD. The best suited empirical model is then selected as per statistical criteria with lower values of standard error, standard deviation, mean absolute deviation (MAD), root mean square error (RMSE) and higher values of R square and correlation coefficient. Statistical significance of selected empirical model was evaluated by paired t test, F test and P value at 95 % confidence level. The developed relationship is then compared with the existing Khosla and Inglis and DeSouza relationships. Outcome of this comparison produces encouraging inferences to suggest an effective regional relationship for annual runoff estimation in the Indravati sub basin of Godavari river in India.
Rainfall-Runoff Modelling using Modified NRCS-CN,RS and GIS -A Case StudyIJERA Editor
Study of rainfall and runoff for any area and modeling it, is one of the important aspects for planning and
development of water resources. The development of water resources and its effective management plays a vital
role in development of any country more particularly in India, which is an agricultural based economy. Hence it
is intended to develop a model of Rainfall and runoff to a river basin and also apply the methodology to Sarada
River Basin which has drainage area of 1252.99 Sq.km. The basin is situated in Vishakhapatnam district of
Andhra Pradesh, India. The rainfall and runoff data has been collected from the gauging stations of the basin
apart from rainfall data from nearby stations. MNRCS-CN method has been adopted to calculate runoff. Various
hydrological parameters like soil information, rainfall, land use and land cover (LU/LC) were considered to use
in MNRCS-CN method. The depth of runoff has been computed for different land use patterns using, IRS-P4-
LISS IV data for the study area. Based on the analysis, land use/land cover pattern of Sarada River Basin has
been prepared. The land use/land cover patterns were also visually interpreted and digitized using ERDAS
IMAGINE software. The raster data was processed in ERDAS and geo-referenced and various maps viz. LU/LC
maps, drainage map, contour map, DEM (Digital elevation model) have been generated apart from rainfall
potential map using GIS tool. The estimated runoff using MNRCS-CN model has been simulated and compared
with that of actual runoff. The performance of the model is found to be good for the data considered. The
coefficient of determination R2
value for the observed runoff and that of the computed runoff is found to be
more than 0.72 for the selected watershed basin.
Comparative Analysis of Empirical Models Derived Groundwater Recharge Estimat...RSIS International
The quantification of water resources is very essentiaal
to water resources management. The Venkatapura Watershed of
Karnataka has been selected for the present study. The
groundwater recharge is determined by using different empirical
models proposed by Chaturvedi, Up Irrigation Research
Institute, Bhattacharjee, Krishna Rao, Sehgal, Kumar and
Sethapathi. According Sehgal formulae average maximum
groundwater recharge of 34.27% observed and based on
Chaturvedi formulae minimum groundwater recharge of 8.04%
is observed. The correlation analysis reveals that Chaturvedi,
UPRI and Kumar and Sethapathi formulas are nearly same. The
present study helps to calculate groundwater recharge without
hydrogeological methods.
EFFICACY OF NEURAL NETWORK IN RAINFALL-RUNOFF MODELLING OF BAGMATI RIVER BASINIAEME Publication
In this paper, rainfall-runoff model of Bagmati river basin has been developed
using the ANN Technique. Three-layered fced forward network structure with back
propagation algorithm was used to train the ANN model. Different combinations of
rainfall and runoff were considered as input to the network and trained by BP
algorithm with different error tolerance, learning parameter, number of cycles and
number of hidden layers. The sensitivity of the prediction accuracy to the number of
hidden layer neurons in a back error propagation algorithm was used for the study.
The monthly rainfall and runoff data from 2000 to 2009 of Bagmati river basin has
been considered for the development of ANN model. Performance evaluation of the
model has been done by using statistical parameters. Three sets of data have been
used to make several combination of year keeping in view the highest peaks of
hydrographs. First set of data used was from 2000 to 2006 for the calibration and
from 2007 to 2009 for validation. The second set of data was from 2004 to 2009 for
calibration and from 2000 to 2003 for validation. The Third set of data was from 2000
to 2009 for calibration and from 2007 to 2009 for validation. It was found that the
third set of data gave better result than other two sets of data. The study demonstrates
the applicability of ANN approach in developing effective non-linear models of
Rainfall-Runoff process without the need to explicitly representing the internal
hydraulic structure of the watershed
Derivation Of Intensity Duration Frequency Curves Using Short Duration Rainfa...Mohammed Badiuddin Parvez
The estimation of rainfall intensity is commonly required for the design of hydraulic and water resources engineering control structures. The intensity-duration-frequency (IDF) relationship is a mathematical relationship between the rainfall intensity, the duration and the return period. The present study aimed the derivation of IDF curves of Yermarus Raingauge Station of Raichur District with 19 years of rainfall data (1998 to 2016). The Normal Distribution, Log Normal Distribution, Gumbel distribution, Pearson Type III Distribution and Log Pearsons Type III Distribution techniques are used to Find the rainfall intensity values of 2, 5, 10, 15, 30, 60, 120, 720, 1440 minutes of rainfall duration with different return period. Chi Square test was conducted to find the goodness of fit the short duration IDF using daily rainfall data are presented, which is input for water resources projects.
ASSESSING THE EFFECTS OF SPATIAL INTERPOLATION OF RAINFALL ON THE STREAMFLOW ...civej
Precipitation within a river basin varies spatially and temporally and hence, is the most relevant input for
hydrologic modelling. Various interpolation methods exist to distribute rainfall spatially within a basin.
The sparse distribution of raingauge stations within a river basin and the differences in interpolation
methods can potentially impact the streamflow simulated using a hydrologic model. The present study
focuses on assessing the effect of spatial interpolation of rainfall using Theissen polygon, Inverse distance
weighted (IDW) method and Ordinary Kriging on the streamflow simulated using a physically based
spatially distributed model-SHETRAN in Vamanapuram river basin in Southern Kerala, India. The
SHETRAN model in the present study utilises rainfall data from the available rain gauge stations within the
basin and potential evapo-transpiration calculated using Penman-Monteith method, along with other input
parameters like soil and landuse. Four years of rainfall and evapo-transpiration data on a daily scale is
used for model calibration and one year data for validation. The performance of the different spatial
interpolation methods were assessed based on the Mean Annual flow and statistical parameters like NashSutcliffe
Efficiency, coefficient of determination. The ordinary kriging and IDW methods were found to be
satisfactory in the spatial interpolation of rainfall.
Determination of homogenous regions in the Tensift basin (Morocco).IJERA Editor
The aim of this study is to determine homogenous region in the Tensift basin within which the hydrological behavior is similar. In order to do this we used two methods: The Principal components analysis on the monthly precipitation registered at the 23 rainfall stations. This resulted in setting apart 4 groups of stations. The second method is analysis of land use map, geological map, pedagogical map, vegetation map and slope map of the studied area. This method allowed us to delineate 4 homogenous areas. The two methods yielded complementary results and the superposition of groups and regions obtained allowed us to retain 4 homogenous regions corresponding to 3 groups of stations.
Assessing Water Demand And Supply For Srinagar City (J&K) India, Under Chang...IJMER
The study holds significance keeping in view the global climatic concerns, which began
to cast their shadows on the climate of Jammu and Kashmir as well. In order to accomplish the
present study, WEAP (water evaluation and planning model) of Stockholm Environment Institute
was used. This model is a tool for integrated water resource management and planning like,
forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas
and Hydropower generation, etc,. During the present study discharge data from 1979-2010 (past
thirty years) of our study rivers i.e., Dachigam Stream and Sindh Stream was used as supply to our
demand sites and also to find the impacts of changing climatic conditions over them. Due to
availability of data upto year 2010 only therefore the scenarios were generated from year 2011
onwards. The water demands for Srinagar i,e., irrigation demands for agriculture and water
supply demands for our domestic needs was analyzed, industrial demands were not analyzed as we
have negligible demands in this sector. The water supplied to our demand sites was mostly
contributed by our study rivers and a little demand was met by ground water. Data was collected
from various agencies like PHE Srinagar, Census data of 2011, Meteorology department etc. This
collected and generated data was given as input to the WEAP model. The model generated the
trends for discharge of our study rivers for next 15 years and at the same time also generated
scenarios calculating our demands and supplies for the future. The model results reveal that there
will be shortages in the requirements met in the urban water needs for some years like 2016, 2017,
2018 and 2020. The results generated from the model outputs will help us in predicting whether
our water resources are going to suffice our growing water needs or not in future. The results will
help in drafting policies for future regarding water supplies and demands under changing climatic
scenarios.
WATERSHED MODELING USING ARTIFICIAL NEURAL NETWORKS IAEME Publication
Artificial Neural Networks analysis was used for modeling rainfall-runoff relationship. A new Instantaneous ANN watershed model was built and tried herein using Walnut Gulch watershed (catchment) area. For modeling the instantaneous response of a catchment to a rainfall event an ANN model was built shown herein. The built model can represent the actual response using descritized
rainfall-runoff values, over a selected time interval (∆t). As this time interval decreases the actual response is more accurately modeled. This model was applied to one of the sub-catchment of Walnut Gulch watershed (sub-catchment No.9 (flume 11)). The model was found successful to represent the lag-time and time of runoff related to the hyetograph properties
Rainfall-Runoff Modelling using Modified NRCS-CN,RS and GIS -A Case StudyIJERA Editor
Study of rainfall and runoff for any area and modeling it, is one of the important aspects for planning and
development of water resources. The development of water resources and its effective management plays a vital
role in development of any country more particularly in India, which is an agricultural based economy. Hence it
is intended to develop a model of Rainfall and runoff to a river basin and also apply the methodology to Sarada
River Basin which has drainage area of 1252.99 Sq.km. The basin is situated in Vishakhapatnam district of
Andhra Pradesh, India. The rainfall and runoff data has been collected from the gauging stations of the basin
apart from rainfall data from nearby stations. MNRCS-CN method has been adopted to calculate runoff. Various
hydrological parameters like soil information, rainfall, land use and land cover (LU/LC) were considered to use
in MNRCS-CN method. The depth of runoff has been computed for different land use patterns using, IRS-P4-
LISS IV data for the study area. Based on the analysis, land use/land cover pattern of Sarada River Basin has
been prepared. The land use/land cover patterns were also visually interpreted and digitized using ERDAS
IMAGINE software. The raster data was processed in ERDAS and geo-referenced and various maps viz. LU/LC
maps, drainage map, contour map, DEM (Digital elevation model) have been generated apart from rainfall
potential map using GIS tool. The estimated runoff using MNRCS-CN model has been simulated and compared
with that of actual runoff. The performance of the model is found to be good for the data considered. The
coefficient of determination R2
value for the observed runoff and that of the computed runoff is found to be
more than 0.72 for the selected watershed basin.
Comparative Analysis of Empirical Models Derived Groundwater Recharge Estimat...RSIS International
The quantification of water resources is very essentiaal
to water resources management. The Venkatapura Watershed of
Karnataka has been selected for the present study. The
groundwater recharge is determined by using different empirical
models proposed by Chaturvedi, Up Irrigation Research
Institute, Bhattacharjee, Krishna Rao, Sehgal, Kumar and
Sethapathi. According Sehgal formulae average maximum
groundwater recharge of 34.27% observed and based on
Chaturvedi formulae minimum groundwater recharge of 8.04%
is observed. The correlation analysis reveals that Chaturvedi,
UPRI and Kumar and Sethapathi formulas are nearly same. The
present study helps to calculate groundwater recharge without
hydrogeological methods.
EFFICACY OF NEURAL NETWORK IN RAINFALL-RUNOFF MODELLING OF BAGMATI RIVER BASINIAEME Publication
In this paper, rainfall-runoff model of Bagmati river basin has been developed
using the ANN Technique. Three-layered fced forward network structure with back
propagation algorithm was used to train the ANN model. Different combinations of
rainfall and runoff were considered as input to the network and trained by BP
algorithm with different error tolerance, learning parameter, number of cycles and
number of hidden layers. The sensitivity of the prediction accuracy to the number of
hidden layer neurons in a back error propagation algorithm was used for the study.
The monthly rainfall and runoff data from 2000 to 2009 of Bagmati river basin has
been considered for the development of ANN model. Performance evaluation of the
model has been done by using statistical parameters. Three sets of data have been
used to make several combination of year keeping in view the highest peaks of
hydrographs. First set of data used was from 2000 to 2006 for the calibration and
from 2007 to 2009 for validation. The second set of data was from 2004 to 2009 for
calibration and from 2000 to 2003 for validation. The Third set of data was from 2000
to 2009 for calibration and from 2007 to 2009 for validation. It was found that the
third set of data gave better result than other two sets of data. The study demonstrates
the applicability of ANN approach in developing effective non-linear models of
Rainfall-Runoff process without the need to explicitly representing the internal
hydraulic structure of the watershed
Derivation Of Intensity Duration Frequency Curves Using Short Duration Rainfa...Mohammed Badiuddin Parvez
The estimation of rainfall intensity is commonly required for the design of hydraulic and water resources engineering control structures. The intensity-duration-frequency (IDF) relationship is a mathematical relationship between the rainfall intensity, the duration and the return period. The present study aimed the derivation of IDF curves of Yermarus Raingauge Station of Raichur District with 19 years of rainfall data (1998 to 2016). The Normal Distribution, Log Normal Distribution, Gumbel distribution, Pearson Type III Distribution and Log Pearsons Type III Distribution techniques are used to Find the rainfall intensity values of 2, 5, 10, 15, 30, 60, 120, 720, 1440 minutes of rainfall duration with different return period. Chi Square test was conducted to find the goodness of fit the short duration IDF using daily rainfall data are presented, which is input for water resources projects.
ASSESSING THE EFFECTS OF SPATIAL INTERPOLATION OF RAINFALL ON THE STREAMFLOW ...civej
Precipitation within a river basin varies spatially and temporally and hence, is the most relevant input for
hydrologic modelling. Various interpolation methods exist to distribute rainfall spatially within a basin.
The sparse distribution of raingauge stations within a river basin and the differences in interpolation
methods can potentially impact the streamflow simulated using a hydrologic model. The present study
focuses on assessing the effect of spatial interpolation of rainfall using Theissen polygon, Inverse distance
weighted (IDW) method and Ordinary Kriging on the streamflow simulated using a physically based
spatially distributed model-SHETRAN in Vamanapuram river basin in Southern Kerala, India. The
SHETRAN model in the present study utilises rainfall data from the available rain gauge stations within the
basin and potential evapo-transpiration calculated using Penman-Monteith method, along with other input
parameters like soil and landuse. Four years of rainfall and evapo-transpiration data on a daily scale is
used for model calibration and one year data for validation. The performance of the different spatial
interpolation methods were assessed based on the Mean Annual flow and statistical parameters like NashSutcliffe
Efficiency, coefficient of determination. The ordinary kriging and IDW methods were found to be
satisfactory in the spatial interpolation of rainfall.
Determination of homogenous regions in the Tensift basin (Morocco).IJERA Editor
The aim of this study is to determine homogenous region in the Tensift basin within which the hydrological behavior is similar. In order to do this we used two methods: The Principal components analysis on the monthly precipitation registered at the 23 rainfall stations. This resulted in setting apart 4 groups of stations. The second method is analysis of land use map, geological map, pedagogical map, vegetation map and slope map of the studied area. This method allowed us to delineate 4 homogenous areas. The two methods yielded complementary results and the superposition of groups and regions obtained allowed us to retain 4 homogenous regions corresponding to 3 groups of stations.
Assessing Water Demand And Supply For Srinagar City (J&K) India, Under Chang...IJMER
The study holds significance keeping in view the global climatic concerns, which began
to cast their shadows on the climate of Jammu and Kashmir as well. In order to accomplish the
present study, WEAP (water evaluation and planning model) of Stockholm Environment Institute
was used. This model is a tool for integrated water resource management and planning like,
forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas
and Hydropower generation, etc,. During the present study discharge data from 1979-2010 (past
thirty years) of our study rivers i.e., Dachigam Stream and Sindh Stream was used as supply to our
demand sites and also to find the impacts of changing climatic conditions over them. Due to
availability of data upto year 2010 only therefore the scenarios were generated from year 2011
onwards. The water demands for Srinagar i,e., irrigation demands for agriculture and water
supply demands for our domestic needs was analyzed, industrial demands were not analyzed as we
have negligible demands in this sector. The water supplied to our demand sites was mostly
contributed by our study rivers and a little demand was met by ground water. Data was collected
from various agencies like PHE Srinagar, Census data of 2011, Meteorology department etc. This
collected and generated data was given as input to the WEAP model. The model generated the
trends for discharge of our study rivers for next 15 years and at the same time also generated
scenarios calculating our demands and supplies for the future. The model results reveal that there
will be shortages in the requirements met in the urban water needs for some years like 2016, 2017,
2018 and 2020. The results generated from the model outputs will help us in predicting whether
our water resources are going to suffice our growing water needs or not in future. The results will
help in drafting policies for future regarding water supplies and demands under changing climatic
scenarios.
WATERSHED MODELING USING ARTIFICIAL NEURAL NETWORKS IAEME Publication
Artificial Neural Networks analysis was used for modeling rainfall-runoff relationship. A new Instantaneous ANN watershed model was built and tried herein using Walnut Gulch watershed (catchment) area. For modeling the instantaneous response of a catchment to a rainfall event an ANN model was built shown herein. The built model can represent the actual response using descritized
rainfall-runoff values, over a selected time interval (∆t). As this time interval decreases the actual response is more accurately modeled. This model was applied to one of the sub-catchment of Walnut Gulch watershed (sub-catchment No.9 (flume 11)). The model was found successful to represent the lag-time and time of runoff related to the hyetograph properties
Utilitas Mathematica Journal has become a fully open-access journal. This journal publishes mainly in areas of pure and applied mathematics, Statistics. Our journal is an official publication of the Utilitas mathematical journal’s original research articles and aspects of both pure and applied mathematics.
Utilitas Mathematica Journal has become a fully open-access journal. This journal publishes mainly in areas of pure and applied mathematics, Statistics. Our journal is an official publication of the Utilitas mathematical journal’s original research articles and aspects of both pure and applied mathematics.
Comparison and Evaluation of Support Vector Machine and Gene Programming in R...AI Publications
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Statistical analysis of an orographic rainfall for eight north-east region of...IJICTJOURNAL
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On the performance analysis of rainfall prediction using mutual information...IJECEIAES
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Binary classification of rainfall time-series using machine learning algorithmsIJECEIAES
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RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have anaccurate model for rainfall prediction. Recently, several data-driven modeling approaches havebeen investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third of the data was used for training the model and One-third for testing.
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Model is a system, by whose operation; the characteristics of other similar systems can be ascertained. Experimental observation made on a model bear a definite relationship with prototype. So, the model analysis or modeling is actually an experimental method of finding solution of complex flow problems like surface water modeling, sub-surface water modeling etc. Many flow situations are not amenable to theoretical analysis. Modeling is a valuable means of obtaining better understanding of particular situation. Inspired by the functioning of the brain and biological nervous system, Artificial Neural Networks (ANNs) has been applied to various hydrological problems in last two decades. In this study, two ANN models using feed forward – back propagation network are developed to correlate a relationship between rainfall and runoff on monthly and weekly basis for Kali river catchment up to Supa dam in Uttara Kannada District of Karnataka State, India. The developed two models are compared and evaluated using standard statistical parameters to know strength and weaknesses. This performance can be further refined by incorporating more input parameters of catchment properties like soil moisture index; land use and land cover details etc.
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RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Performance improvement of a Rainfall Prediction Model using Particle Swarm O...ijceronline
The performances of the statistical methods of time series forecast can be improved by precise selection of their parameters. Various techniques are being applied to improve the modeling accuracy of these models. Particle swarm optimization is one such technique which can be conveniently used to determine the model parameters accurately. This robust optimization technique has already been applied to improve the performance of artificial neural networks for time series prediction. This study uses particle swarm optimization technique to determine the parameters of an exponential autoregressive model for time series prediction. The model is applied for annual rainfall prediction and it shows a fairly good performance in comparison to the statistical ARIMA model
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Comparative Study of Machine Learning Algorithms for Rainfall Predictionijtsrd
Majority of Indian framers depend on rainfall for agriculture. Thus, in an agricultural country like India, rainfall prediction becomes very important. Rainfall causes natural disasters like flood and drought, which are encountered by people across the globe every year. Rainfall prediction over drought regions has a great importance for countries like India whose economy is largely dependent on agriculture. A sufficient data length can play an important role in a proper estimation drought, leading to a better appraisal for drought risk reduction. Due to dynamic nature of atmosphere statistical techniques fail to provide good accuracy for rainfall prediction. So, we are going to use Machine Learning algorithms like Multiple Linear Regression, Random Forest Regressor and AdaBoost Regressor, where different models are going to be trained using training data set and tested using testing data set. The dataset which we have collected has the rainfall data from 1901 2015, where across the various drought affected states. Nonlinearity of rainfall data makes Machine Learning algorithms a better technique. Comparison of different approaches and algorithms will increase an accuracy rate of predicting rainfall over drought regions. We are going to use Python to code for algorithms. Intention of this project is to say, which algorithm can be used to predict rainfall, in order to increase the countries socioeconomic status. Mylapalle Yeshwanth | Palla Ratna Sai Kumar | Dr. G. Mathivanan M.E., Ph.D ""Comparative Study of Machine Learning Algorithms for Rainfall Prediction"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22961.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22961/comparative-study-of-machine-learning-algorithms-for-rainfall-prediction/mylapalle-yeshwanth
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Assessment of two Methods to study Precipitation Prediction
1. International journal of Horticulture, Agriculture and Food science(IJHAF) [Vol-1, Issue-2, July-Aug, 2017]
AI Publications ISSN: 2456-8635
22|Pagewww.aipublications.com/ijhaf
Assessment of two Methods to study Precipitation
Prediction
Mohammad Valipour
Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
E-mail: mohammad25mordad@yahoo.com
Abstract— Presipitation analysis plays an important role
in hydrological studies. In this study, using 50 years of
rainfall data and ARIMA model, critical areas of Iran were
determined. For this purpose, annual rainfall data of 112
different synoptic stations in Iran were gathered. To
summarize, it could be concluded that: ARIMA model was
an appropriate tool to forecast annual rainfall. According
to obtained results from relative error, five stations were in
critical condition. At 45 stations accrued rainfalls with
amounts of less than half of average in the 50-year period.
Therefore, in these 45 areas, chance of drought is more
than other areas of Iran.
Keywords— Rainfall, Hydrological models,
Forecasting.
I. INTRODUCTION
In this study, ARIMA model forecasted annual rainfall in
112 different synoptic stations in Iran and critical areas
were determined. After publishing the paper of Box and
Jenkins (1976), Box-Jenkins models became one general
time series model of hydrological forecasting. These
models include Auto Regressive Integrated Moving
Average (ARIMA), Auto Regressive Moving Average
(ARMA), Auto Regressive (AR), and Moving Average
(MA). Access to basic information requires integration
from the series (for a continuous series) or calculating all of
differences the series (for a continuous series). Since the
constant of integration in derivation or differences deleted,
the probability of using these amount or middle amount in
this process is not possible. Therefore, ARIMA models are
non-static and cannot be used to reconstruct the missing
data. However, these models are very useful for forecasting
changes in a process (Karamouz and Araghinejad, 2012).
Models of time series analysis (Box-Jenkins models) and
drought periods study in various fields of hydrology and
rainfall forecasting in irrigation schedule are widely
applied, which some of them will be described in the
following.
Mishra and Singh (2011) did a review about drought
modeling. Smakhtin and Hughes (2007) described a new
software package for automated estimation, display, and
analyses of various drought indices–continuous functions
of precipitation that allow quantitative assessment of
meteorological drought events to be made. Yurekli and
Kurunc (2006) simulated agricultural drought periods based
on daily rainfall and crop water consumption. Constituted
monthly time series of drought durations of each
hydrologic homogeneous section was simulated using
ARIMA model. No linear trend was observed for the time
series except one section. In general, the predicted data
from the selected best models for the time series of each
section represented the actual data of that section. Serinaldi
and Kilsby (2012) presented a modular class of multisite
monthly rainfall generators for water resource management
and impact studies. The results of the case study point out
that the model can capture several characteristics of the
rainfall series. In particular, it enables the simulation of low
and high rainfall scenarios more extreme than those
observed as well as the reproduction of the distribution of
the annual accumulated rainfall, and of the relationship
between the rainfall and circulation indices such as North
Atlantic Oscillation (NAO) and Sea Surface Temperature
(SST), thus making the framework well-suited for
sensitivity analysis under alternative climate scenarios and
additional forcing variables. Luc et al. (2001) studied an
application of artificial neural networks for rainfall
forecasting successfully. Wei et al. (2006) using weather
satellite imagery forecasted rainfall in Taiwan. Andrieu et
al. (1996) studied Adaptation and application of a
quantitative rainfall forecasting model in a mountainous
region. This work shows that a limit on forecast lead-time
may be related to the response time of the precipitating
cloud system. Burlando et al. (1993) using ARMA models
forecasted short-term rainfall. Hourly rainfall from two
gaging stations in Colorado, USA, and from several
stations in Central Italy been used. Results showed that the
event-based estimation approach yields better forecasts. Hu
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et al. (2006) studied rainfall, mosquito density and the
transmission of Ross River virus using a time-series
forecasting model. Their results showed that both rainfall
and mosquito density were strong predictors of the Ross
River virus transmission in simple models. Ramírez et al.
(2005) used artificial neural network technique for rainfall
forecasting applied to the São Paulo region. The results
showed that ANN forecasts were superior to the ones
obtained by the linear regression model thus revealing a
great potential for an operational suite. Han et al. (2010)
forecasted drought based on the remote sensing data using
ARIMA model successfully. Chattopadhyay and
Chattopadhyay (2010) compared ARIMA and ARNN
models using Univariate modelling of summer-monsoon
rainfall time series. Anctil et al. (2004) survived impact of
the length of observed records on the performance of ANN
and of conceptual parsimonious rainfall-runoff forecasting
models. The results showed that best performance about
evenly for 3- and 5-year training sets, but multiple-layer
perceptrons (MLPs) did better whenever the training set
was dominated by wet weather. The MLPs continued to
improve for input vectors of 9 years and more, which was
not the case of the conceptual model. Jia and Culver (2006)
using bootstrapped artificial neural networks suggested that
even a small set of periodic instantaneous observations of
stage from a staff gauge, which can easily be collected by
volunteers, can be a useful data set for effective
hydrological modeling. M. Baareh et al. (2006) used the
artificial neural network and Auto-Regression (AR) models
to the river flow forecasting problem. A comparative study
of both ANN and the AR conventional model networks
indicated that the artificial neural networks performed
better than the AR model. They showed that ANN models
can be used to train and forecast the daily flows of the
Black Water River near Dendron in Virginia and the Gila
River near Clifton in Arizona. Xiong and M. O'connor
(2002) used four different error-forecast updating models,
autoregressive (AR), autoregressive-threshold (AR-TS),
fuzzy autoregressive-threshold (FU-AR-TS), and artificial
neural network (ANN) to the real-time river flow
forecasting. They found that all of these four updating
models are very successful in improving the flow forecast
accuracy. Chenoweth et al. (2000) estimated the ARMA
model parameters using neural networks. Their results
showed that the ability of neural networks to accurately
identify the order of an ARMA model was much lower than
reported by previous researchers, and is especially low for
time series with fewer than 100 observations. Using
forecasting of hydrologic time series with ridge regression
in feature space, Yu and Liong (2007) showed that the
training speed in data mining method was very much faster
than ARIMA model. See and Abrahart (2001) used of data
fusion for hydrological forecasting. Their results showed
that using of data fusion methodologies for ANN, fuzzy
logic, and ARMA models accuracy of forecasting would
increase. Using hybrid approaches, Srinivas and Srinivasan
(2000) improved the accuracy of AR model parameters for
annual streamflows. Using the Fourier coefficients, Ludlow
and Enders (2000) estimated the ARMA model parameters
with a relatively good accuracy. Chenoweth et al. (2004)
estimated the ARMA model parameters using the Hilbert
coefficients. Their results showed that the Hilbert
coefficients are considered a useful tool for estimating
ARMA model parameters. Balaguer et al. (2008) used the
method of time delay neural network (TDNN) and ARMA
model to forecast asking for help in support centers for
crisis management. The obtained correlation results for
TDNN model and ARMA were 0.88 and 0.97, respectively.
This study confirmed the superiority of ARMA model to
the TDNN. Toth et al. (2000) used the artificial neural
network and ARMA models to forecast rainfall. The results
show the success of both short-term rainfall-forecasting
models for forecast floods in real time. Mohammadi et al.
(2005) forecast Karaj reservoir inflow using data of melting
snow and artificial neural network and ARMA methods,
and regression analysis. 60% of inflow in dam happens
between Aprils until June, so forecasting the inflow in this
season is very important for dam’s performance. The
highest inflows were in the spring due to the snow melt
caused by draining in threshold winter. The results showed
that artificial neural network has lower significant errors as
compared with other methods. Mohammadi et al. (2006) in
other research estimated parameters of an ARMA model
for river flow forecasting using goal programming. Their
results showed that the goal programming is a precise and
effective method for estimating ARMA model parameters
for forecasting inflow. Valipour et al. (2012) estimated
parameters of ARMA and ARIMA models and compare
their ability for inflow forecasting. By comparing root
mean square error of the model, it was determined that
ARIMA model can forecast inflow to the Dez reservoir
from 12 months ago with lower error than the ARMA
model. Valipour (2012) studied number of required
observation data for rainfall forecasting according to the
climate conditions. By comparing R2 of the models, it was
determined that time series models were better appropriate
to rainfall forecasting in semi-arid climate. Numbers of
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required observation data for forecasting of one next year
were 60 rainfall data in semi-arid climate.
Therefore, considering the above mentioned performed
researches, we can know the efficacy of ARIMA model in
forecasting field and hydrologic sampling. Effect of annual
rainfall forecasting has not been done in previous
researches for agriculture water management and critical
areas determining. This study aims to forecast annual
rainfall using ARIMA model and determine areas that
chance of drought in those is more than other areas of Iran.
II. MATERIALS AND METHODS
In this study to forecasting of annual rainfall used from 112
synoptic stations data in Iran. In order to rainfall
forecasting at the annual scale, rainfall data period from
1951-2000 has been gathered. Actually, the used data
involved 5600 data (all stations). In this study, ARIMA
model were used for forecast annual rainfall. In each station
250 structure of ARIMA model were used. For this purpose
used MINITAB software to run of all ARIMA structures.
In this research used from 49 years data (1951-1999) for
calibration of ARIMA model and forecasted amount of
annual rainfall for year 2000. Finally, by two methods
critical areas of Iran for water management were specified
and used relative error to compare stations. In first method,
areas that amount of their relative error were more than
20% were introduced as critical areas. In second method,
areas that amount of their rainfall in some years were less
than half of average rainfalls in 50 years periods were
specified as areas that chance of drought in these were
more that other areas.
III. RESULTS AND DISCUSSION
Tables 1 to 5 shows obtained relative error for 112 different
stations with stations information and best structures of
ARIMA models. Figure 1 represents ability of ARIMA
model in annual rainfall forecasting. Figures 2 and 3 shows
critical areas of Iran for agriculture water management
according to first and second methods, respectively.
After running 28000 ARIMA structures for all stations,
according to obtained results from relative error in tables 1
to 5, five stations include IRANSHAHR, SIRJAN, NAEIN,
ZAHEDAN, and KISH, were in critical condition. In these
areas due to very low rainfalls in 2000, ARIMA model do
not give a good forecasting and relative error was more
than 20%. Therefore, in these areas due to lack of accurate
forecasting, agriculture water management and crop pattern
presenting must be done very carefully. As the figure 1 in
65% from forecasted annual rainfalls by ARIMA model
amount of relative error was less than 0.1 (10%). These
areas were in the safe range. 35% of forecasting had a
relative error between 0.1-0.2 (10-20%) and these areas
were in the alarm range. Finally only 5% of all ARIMA
forecasting occurred in the critical range. This showed a
high ability of ARIMA model in annual rainfall forecasting.
In addition five areas marked in the first method, can be
determined 45 areas as critical areas of Iran due to occurred
amount of their rainfall in some years were less than half of
average rainfalls in 50 years periods. In these areas because
observed very low rainfall in some cases, drought in the
coming years is not unexpected. Thus, how agriculture
water management should be performed with high accuracy
and proposed crop pattern to be applied with adequate
safety factors else there is the possibility of being trapped
in periods of drought. To support of sustainable agriculture
and management of required water can be prevented from
future damage.
Table.1: Obtained relative error for 112 different stations with stations information and best structures of ARIMA models (0-
3%)
Station Code Altitude Longitude
Elevation
(m)
Actual
rainfall
(mm/year)
Forecasted
rainfall
(mm/year)
Relative
error
(%) Best model
MESHKINSHAR 40705 38 23 N 47 40 E 1568.5 289.4 289.0 0.1 ARIMA(1,0,0)
BABOLSAR 40736 36 43 N 52 39 E -21.0 968.4 964.5 0.4 ARIMA(5,1,3)
RAMHORMOZ 40813 31 16 N 49 36 E 150.5 292.8 291.4 0.5 ARIMA(4,1,0)
TORBATE JAM 40806 35 15 N 60 35 E 950.4 111.6 111.0 0.6 ARIMA(1,3,0)
ABADAN 40831 30 22 N 48 15 E 6.6 155.5 156.7 0.8 ARIMA(5,1,0)
MAKOO 40701 39 20 N 44 26 E 1411.3 185.7 184.2 0.8 ARIMA(0,0,2)
SHOSHTAR 99446 32 3 N 48 50 E 67.0 296.3 298.7 0.8 ARIMA(1,1,0)
ZANJAN 40729 36 41 N 48 29 E 1663.0 309.7 312.7 1.0 ARIMA(5,1,0)
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NOUSHAHR 40734 36 39 N 51 30 E -20.9 1227.2 1239.4 1.0 ARIMA(1,1,0)
ARDESTAN 40799 33 23 N 52 23 E 1252.4 129.2 130.5 1.0 ARIMA(5,1,1)
ALIGOODARZ 40783 33 24 N 49 41 E 2034.0 415.1 409.1 1.4 ARIMA(1,1,3)
KANGAVAR 40771 34 30 N 48 0 E 1460.0 346.8 352.0 1.5 ARIMA(1,1,0)
SHIRAZ 40848 29 36 N 52 32 E 1488.0 358.0 351.7 1.8 ARIMA(4,1,0)
KARAJ 40752 35 55 N 50 54 E 1312.5 240.0 244.3 1.8 ARIMA(1,1,0)
ARAK 40769 34 6 N 49 46 E 1708.0 343.7 337.5 1.8 ARIMA(5,1,0)
BOJNURD 40723 37 28 N 57 19 E 1091.0 309.1 301.6 2.4 ARIMA(3,3,4)
KHOY 40703 38 33 N 44 58 E 1103.0 207.1 212.2 2.5 ARIMA(4,1,0)
YASOUJ 40836 30 40 N 51 35 E 1837.0 619.5 635.2 2.5 ARIMA(0,0,2)
YAZD 40821 31 54 N 54 24 E 1230.2 44.9 46.1 2.6 ARIMA(1,1,0)
OROOMIEH 40712 37 32 N 45 5 E 1313.0 230.6 236.7 2.6 ARIMA(5,1,1)
KERMAN 40841 30 15 N 56 58 E 1753.8 86.9 89.2 2.6 ARIMA(0,0,1)
ILAM 40780 33 38 N 46 25 E 1363.4 504.0 489.3 2.9 ARIMA(5,1,2)
BOROOJEN 99459 31 57 N 51 18 E 2197.0 175.1 180.4 3.0 ARIMA(5,1,0)
Table.2: Obtained relative error for 112 different stations with stations information and best structures of ARIMA models (3.1-
5.5%)
Station Code Altitude Longitude
Elevation
(m)
Actual rainfall
(mm/year)
Forecasted
rainfall
(mm/year)
Relative
error
(%) Best model
GORGAN 40738 36 51 N 54 16 E 13.3 579.0 561.0 3.1 ARIMA(1,1,0)
AHWAZ 40811 31 20 N 48 40 E 22.5 234.8 227.4 3.1 ARIMA(1,0,1)
SARDASHT 40725 36 9 N 45 30 E 1670.0 689.1 712.0 3.3 ARIMA(1,1,0)
KHORRAMABAD 40782 33 29 N 48 22 E 1125.0 423.8 438.6 3.5 ARIMA(5,1,2)
SARAKHS 40741 36 32 N 61 10 E 235.0 99.3 95.8 3.6 ARIMA(5,3,2)
TABRIZ 40706 38 5 N 46 17 E 1361.0 205.0 197.6 3.6 ARIMA(5,1,0)
KHALKHAL 40717 37 38 N 48 31 E 1796.0 340.7 353.1 3.6 ARIMA(5,1,1)
GHOOCHAN 40740 37 4 N 58 30 E 1287.0 271.5 281.4 3.6 ARIMA(4,1,0)
BANDAR
ANZALI 40718 37 28 N 49 28 E -26.2 2009.8 1934.1 3.8 ARIMA(5,1,4)
BIJAR 40748 35 53 N 47 37 E 1883.4 309.4 321.3 3.9 ARIMA(5,1,4)
ABADEH 40818 31 11 N 52 40 E 2030.0 95.1 99.2 4.3 ARIMA(5,1,1)
MALAYER 40775 34 17 N 48 49 E 1725.0 327.4 313.4 4.3 ARIMA(4,1,0)
SAVEH 99372 35 3 N 50 20 E 1108.0 239.2 228.4 4.5 ARIMA(1,2,0)
KERMANSHAH 40766 34 17 N 47 7 E 1322.0 352.4 335.8 4.7 ARIMA(1,1,0)
SHAHROUD 40739 36 25 N 54 57 E 1345.3 166.9 158.9 4.8 ARIMA(1,1,0)
MASJED
SOLEYMAN 40812 31 56 N 49 17 E 320.5 372.2 390.4 4.9 ARIMA(1,1,0)
ESLAMABAD
GHARB 40779 34 8 N 46 26 E 1346.0 354.4 336.3 5.1 ARIMA(4,1,2)
SABZEVAR 40743 36 12 N 57 43 E 977.6 147.4 155.2 5.3 ARIMA(3,1,3)
SEMNAN 40757 35 33 N 53 23 E 1171.0 140.5 148.0 5.4 ARIMA(1,1,0)
GHAZVIN 40731 36 15 N 50 0 E 1278.3 311.0 294.2 5.4 ARIMA(1,1,0)
GHORVEH 40772 35 10 N 47 48 E 1906.0 317.3 334.6 5.5 ARIMA(1,1,0)
SANANDAJ 40747 35 20 N 47 0 E 1373.4 329.5 311.5 5.5 ARIMA(1,1,0)
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Table.3: Obtained relative error for 112 different stations with stations information and best structures of ARIMA models (5.6-
9.1%)
Station Code Altitude Longitude
Elevation
(m)
Actual
rainfall
(mm/year)
Forecasted
rainfall
(mm/year)
Relative
error
(%) Best model
ABALI 40755 35 45 N 51 53 E 2465.2 440.9 416.1 5.6 ARIMA(0,0,2)
DOGONBADAN 40835 30 26 N 50 46 E 699.5 336.5 316.5 5.9 ARIMA(1,3,0)
KASHMAR 40763 35 12 N 58 28 E 1109.7 145.7 154.4 5.9 ARIMA(5,1,0)
TEHRAN 40754 35 41 N 51 19 E 1190.8 195.6 183.9 6.0 ARIMA(5,1,1)
KHORRAMDAREH 40730 36 11 N 49 11 E 1575.0 247.9 262.8 6.0 ARIMA4,1,0)
MARIVAN 40750 35 31 N 46 12 E 1287.0 741.5 694.3 6.4 ARIMA(1,1,0)
GARMSAR 40758 35 12 N 52 16 E 825.2 115.1 122.8 6.7 ARIMA(1,1,0)
NEYSHABOOR 40746 36 16 N 58 48 E 1213.0 15.8 16.9 6.7 ARIMA(1,1,0)
IZEH 99455 31 51 N 49 52 E 767.0 600.6 641.5 6.8 ARIMA(5,1,0)
KASHAN 40785 33 59 N 51 27 E 982.3 136.9 146.5 7.0 ARIMA(4,1,0)
SHAHRE KORD 40798 32 20 N 50 51 E 2061.4 242.6 260.0 7.2 ARIMA(1,1,0)
NATANZ 99421 33 32 N 51 54 E 1684.9 194.1 208.5 7.4 ARIMA(1,1,0)
BEHBAHAN 40834 30 36 N 50 14 E 313.0 188.1 202.2 7.5 ARIMA(0,0,1)
BAFGH 40820 31 36 N 55 26 E 991.4 32.2 34.7 7.6 ARIMA(3,1,0)
MARAGHEH 40713 37 24 N 46 16 E 1477.7 175.5 189.0 7.7 ARIMA(1,1,0)
MANJIL 40720 36 44 N 49 24 E 333.0 196.9 212.1 7.7 ARIMA(1,3,0)
TAKAB 40728 36 23 N 47 7 E 1765.0 296.5 272.8 8.0 ARIMA(3,1,2)
GHAEN 40793 33 43 N 59 10 E 1432.0 124.3 134.4 8.1 ARIMA(0,0,1)
BIRJAND 40809 32 52 N 59 12 E 1491.0 94.1 86.4 8.2 ARIMA(0,0,2)
FASSA 40859 28 58 N 53 41 E 1288.3 243.7 264.3 8.5 ARIMA(1,1,0)
KAHNOUJ 40877 27 58 N 57 42 E 469.7 241.3 262.8 8.9 ARIMA(1,5,0)
BUSHEHR 40858 28 59 N 50 50 E 19.6 263.3 287.2 9.1 ARIMA(1,0,1)
GONBADE
GHABOOS 99240 37 15 N 55 10 E 37.2 514.7 467.7 9.1 ARIMA(1,1,0)
Table.4: Obtained relative error for 112 different stations with stations information and best structures of ARIMA models (9.2-
13%)
Station Code Altitude Longitude
Elevation
(m)
Actual
rainfall
(mm/year)
Forecasted
rainfall
(mm/year)
Relative
error (%) Best model
TABASS 40791 33 36 N 56 55 E 711.0 61.2 66.9 9.2 ARIMA(1,0,0)
BANDAR
DAIER 40872 27 50 N 51 56 E 4.0 203.7 183.8 9.8 ARIMA(1,1,0)
JOLFA 40702 38 45 N 45 40 E 736.2 129.2 141.8 9.8 ARIMA(0,0,1)
ZABOL 40829 31 2 N 61 29 E 489.2 26.8 29.4 9.9 ARIMA(0,0,1)
SARAB 40710 37 56 N 47 32 E 1682.0 200.8 220.8 9.9 ARIMA(1,1,0)
GONABAD 40778 34 21 N 58 41 E 1056.0 99.3 89.2 10.1 ARIMA(5,1,0)
MASHHAD 40745 36 16 N 59 38 E 999.2 168.9 151.6 10.3 ARIMA(0,0,3)
FERDOUS 40792 34 1 N 58 10 E 1293.0 101.0 90.4 10.5 ARIMA(5,0,4)
GHOM 40770 34 42 N 50 51 E 877.4 175.1 156.1 10.9 ARIMA(1,0,0)
BOSTAN 40810 31 43 N 48 0 E 7.8 206.2 228.9 11.0 ARIMA(3,1,1)
MIANEH 40716 37 27 N 47 42 E 1110.0 274.6 243.6 11.3 ARIMA(1,1,0)
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MAHABAD 40726 36 46 N 45 43 E 1385.0 313.3 277.5 11.4 ARIMA(4,1,0)
CHAHBAHAR 40898 25 17 N 60 37 E 8.0 44.4 49.6 11.7 ARIMA(2,0,0)
ESFAHAN 40800 32 37 N 51 40 E 1550.4 88.1 77.8 11.7 ARIMA(0,0,2)
BANDAR
MAHSHAHR 40832 30 33 N 49 9 E 6.2 146.2 128.9 11.8 ARIMA(0,0,2)
SAR POL
ZOHAB 40765 34 27 N 45 52 E 545.0 379.5 333.6 12.1 ARIMA(1,1,0)
BAM 40854 29 6 N 58 21 E 1066.9 47.7 53.5 12.1 ARIMA(5,1,1)
GOLPAIGAN 99417 33 28 N 50 17 E 1870.0 184.1 206.9 12.4 ARIMA(1,0,0)
MINAB 40876 27 7 N 57 6 E 27.0 199.0 224.0 12.6 ARIMA(1,2,0)
JASK 40893 25 38 N 57 46 E 4.8 16.4 18.5 12.7 ARIMA(1,3,2)
PIRANSHAHR 40724 36 40 N 45 8 E 1455.0 577.2 503.4 12.8 ARIMA(3,0,3)
ARDEBIL 40708 38 15 N 48 17 E 1332.0 302.8 264.0 12.8 ARIMA(4,1,1)
Table.5: Obtained relative error for 112 different stations with stations information and best structures of ARIMA models
(>13%)
Station Code Altitude Longitude
Elevation
(m)
Actual rainfall
(mm/year)
Forecasted
rainfall
(mm/year)
Relative
error (%) Best model
RAVANSAR 40764 34 43 N 46 40 E 1362.7 399.4 451.6 13.1 ARIMA(5,1,0)
DEHLORAN 40796 32 41 N 47 16 E 232.0 205.5 232.7 13.2 ARIMA(1,0,0)
LAR 40873 27 41 N 54 17 E 792.0 102.1 116.4 14.0 ARIMA(1,0,0)
LORDEGAN 40814 31 31 N 50 49 E 1580.0 466.4 533.9 14.5 ARIMA(3,1,0)
KHASH 40870 28 13 N 61 12 E 1394.0 40.0 45.8 14.5 ARIMA(5,1,0)
RAMSAR 40732 36 54 N 50 40 E -20.0 802.8 920.0 14.6 ARIMA(1,1,0)
BANDAR
ABASS 40875 27 13 N 56 22 E 10.0 213.6 245.3 14.8 ARIMA(5,1,0)
KOOHRANG 40797 32 26 N 50 7 E 2285.0 1077.9 1238.5 14.9 ARIMA(1,3,0)
HAMEDAN 40768 34 51 N 48 32 E 1749.0 318.9 271.4 14.9 ARIMA(1,1,0)
DEZFUL 40795 32 24 N 48 23 E 143.0 429.7 494.9 15.2 ARIMA(4,1,0)
RAFSANJAN 99502 30 25 N 55 54 E 1580.9 52.5 44.5 15.2 ARIMA(3,1,0)
RASHT 40719 37 12 N 49 39 E 36.7 1438.3 1211.7 15.8 ARIMA(2,1,0)
SHAHREZA 40815 31 59 N 51 50 E 1845.2 98.2 115.3 17.4 ARIMA(2,1,0)
TORBATE
HEYDARIEH 40762 35 16 N 59 13 E 1450.8 220.2 259.3 17.8 ARIMA(2,5,3)
BANDAR
LENGEH 40883 26 35 N 54 50 E 14.2 132.1 157.0 18.9 ARIMA(1,1,0)
AHAR 40704 38 26 N 47 4 E 1390.5 243.5 289.6 18.9 ARIMA(5,1,0)
ABOMOOSA 40890 25 50 N 54 50 E 6.6 52.2 62.6 19.8 ARIMA(5,1,0)
KISH 40882 26 30 N 53 59 E 30.0 113.3 136.7 20.7 ARIMA(1,0,0)
ZAHEDAN 40856 29 28 N 60 53 E 1370.0 40.7 49.9 22.6 ARIMA(0,0,2)
NAEIN 40801 32 51 N 53 5 E 1549.0 66.2 91.6 38.3 ARIMA(5,1,0)
SIRJAN 40851 29 28 N 55 41 E 1739.4 66.7 98.9 48.2 ARIMA(3,1,3)
IRANSHAHR 40879 27 12 N 60 42 E 591.1 20.0 33.3 66.4 ARIMA(0,0,3)
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Fig.1: Ability of ARIMA model in rainfall forecasting according to the relative error
Fig.2: Critical areas of Iran for agriculture water management according to first method
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Fig.3: Critical areas of Iran for agriculture water management according to second method
IV. CONCLUSION
In this paper, using 50 years of rainfall data and ARIMA
model, critical areas of Iran were determined. For this
purpose, annual rainfall data of 112 different synoptic
stations in Iran was gathered. To summarize, it could be
concluded that:
According to obtained results from relative error, five
stations include IRANSHAHR, SIRJAN, NAEIN,
ZAHEDAN, and KISH, were in critical condition.
In 45 stations accrued rainfalls with amounts of less than
half of average in 50 years period. Therefore, in these 45
areas chance of drought is more than other areas of Iran.
ARIMA model was an appropriate tool to forecasting
annual rainfall.
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