This document presents a study that uses Bayesian Regularized Neural Networks (BRNN) to model groundwater levels in the Mahabad aquifer in Iran. The study area and data collection process are described. Five factors - precipitation, evaporation, temperature, streamflow, and previous month's groundwater level - are used as inputs to the BRNN model to estimate current groundwater levels. The results show the BRNN model performs excellently with low errors and high accuracy and determination values. Previous month's groundwater level and streamflow are found to be the most important predictors of current groundwater levels.
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.
Projection of future Temperature and Precipitation for Jhelum river basin in ...IJERA Editor
In this paper, downscaling models are developed using a Multiple Linear Regression (MLR) for obtaining projections of mean monthly temperature and precipitation for Jhelum river basin. Precipitation and temperature data are the most frequently used forcing terms in hydrological models. However, the available General Circulation Models (GCMs), which are widely used nowadays to simulate future climate scenarios, do not provide those variables to the need of the models. The purpose of this study is therefore, to apply a statistical downscaling method and assess its strength in reproducing current climate and project future climate. Regression based downscaling technique was usedtodownscaletheCGCM3, HadCM3 and Echam5 GCMpredictionsoftheA1B scenario for the Jhelum river basin located in India. The Multiple Linear Regression (MLR) model shows an increasing trend in temperature in the study area until the end of the 21st century. The average annual temperature showed an increase of 2.37°, 1.50°C and 2.02°C respectively for CGCM3, HadCM3 and Echam5 models over 21st century under A1B scenario. The total annual precipitation decreased by 30.27%, 30.58°C and 36.53% respectively for CGCM3, HadCM3 and Echam5 models over 21st century in A1B scenario using MLR technique. The performance of the linear multiple regression models was evaluated based on several statistical performance indicators.
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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.
Scale-dependency and Sensitivity of Hydrological Estimations to Land Use and ...Beniamino Murgante
Scale-dependency and Sensitivity of Hydrological Estimations to Land Use and Topography for a Coastal Watershed in Mississippi - Vladimir J. Alarcon and Charles G. O’Hara
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.
Projection of future Temperature and Precipitation for Jhelum river basin in ...IJERA Editor
In this paper, downscaling models are developed using a Multiple Linear Regression (MLR) for obtaining projections of mean monthly temperature and precipitation for Jhelum river basin. Precipitation and temperature data are the most frequently used forcing terms in hydrological models. However, the available General Circulation Models (GCMs), which are widely used nowadays to simulate future climate scenarios, do not provide those variables to the need of the models. The purpose of this study is therefore, to apply a statistical downscaling method and assess its strength in reproducing current climate and project future climate. Regression based downscaling technique was usedtodownscaletheCGCM3, HadCM3 and Echam5 GCMpredictionsoftheA1B scenario for the Jhelum river basin located in India. The Multiple Linear Regression (MLR) model shows an increasing trend in temperature in the study area until the end of the 21st century. The average annual temperature showed an increase of 2.37°, 1.50°C and 2.02°C respectively for CGCM3, HadCM3 and Echam5 models over 21st century under A1B scenario. The total annual precipitation decreased by 30.27%, 30.58°C and 36.53% respectively for CGCM3, HadCM3 and Echam5 models over 21st century in A1B scenario using MLR technique. The performance of the linear multiple regression models was evaluated based on several statistical performance indicators.
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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.
Scale-dependency and Sensitivity of Hydrological Estimations to Land Use and ...Beniamino Murgante
Scale-dependency and Sensitivity of Hydrological Estimations to Land Use and Topography for a Coastal Watershed in Mississippi - Vladimir J. Alarcon and Charles G. O’Hara
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 rainfall-runoff model for Chew and Kinder Reservoirs, Peak District; utilising the Flood Studies Report to find whether the dams at Chew and Kinder could withstand a 1-in-10,000 year storm (UK recommended safety limit)
Grade: 91%
Regional Rainfall Frequency Analysis By L-Moments Approach For Madina Region,...IJERDJOURNAL
ABSTRACT:- In arid regions, extreme rainfall event frequency predictions are still a challenging problem, because of the rain gauge stations scarcity and the record length limitation, which are usually short to insure reliable quantile estimates. Regional frequency analysis is one of the popular approaches used to compensate the data limitation. In this paper, regional frequency analysis of maximum daily rainfall is investigated for Madinah province in the Western Kingdom of Saudi Arabia (KSA). The observed maximum daily rainfall records of 20 rainfall stations are selected from 1968 to 2015. The rainfall data is evaluated using four tests, namely, Discordance test (Di), Homogeneity test (H), Goodness of fit test (Zdist) and L-moment ratios diagram (LMRD). The Di of L-moments shows that all the sites belong to one group (Di <3.0).><1). Finally, the Zdist is used to evaluate five probability distribution functions (PDFs) including generalized logistic (GLO), generalized extreme value (GEV), generalized normal (GNO), generalized Pareto (GPA), and Pearson Type III (PE3). Zdist and LMRD both showed that PE3 distribution is the best among the other PDFs. The regional parameters of the candidate PDF are computed using L-moments approach and accordingly the regional dimensionless growth curve is developed. The results enhance the accuracy of extreme rainfall prediction at-sites and also they can be used for ungauged catchment in the region.
A study confined to the lower tapi basin in Gujarat, India to find out the primary causes for 2006 floods in Surat city. The study involves collection of topographical data from the local geological survey organization, rainfall data from meteorological department of india and the application of HEC-HMS software from US Army corps of engineers to identify the primary cause of the runoff.
Presentation at the conference Greenmetrics 2016 of the paper "Geographical Load Balancing across Green Datacenters: a Mean Field Analysis" (authors G. Neglia, M. Sereno, G. Bianchi)
The Development of a Catchment Management Modelling System for the Googong Re...GavanThomas
A scenario assessment model to assist the end-user in determining priorities for a series of agreed management prescriptions that can be enacted through controls on existing landuse
Modelling of Short Duration Isopluvial Map For Raichur District Karnataka Moh...Mohammed Badiuddin Parvez
Everyoneacknowledges that it rains, runoff is generated for a design point of view we should know how much and how often it rains on our project location.Estimation of rainfall intensity is commonly required for the design of hydraulic and water resources engineering control structures. The present study aimed the Estimation of rainfall intensityin Raichur District using twenty five Rain gauge Station with 19 years of rainfall data (1998 to 2016). Log Normal Distribution, techniques are used to derived the rainfall intensity values of 2,5,10,15,30,60,120,720,1440 minutes of rainfall duration with different return period. The short duration IDF using daily rainfall data are presented, which is input for water resources projects. Isopluvial maps were developed for 25years, 50years, 75years and 100years return period
Hydrological Application of Remote – Sensing and GIS for Handling of Excess R...IDES Editor
A GIS based hydrological analysis has been carried
out to explore the possibility of diverting storm runoff
generated from the upper catchment safely through a canal
system constructed at the foothill to avoid flooding at
downstream. The study area consisted of Kalapahar-Udyachal
hills (5.38 km sq) in the Kahilipara- Odalbakra area, situated
in the city of Guwahati, Assam. The Digital Elevation Model
(DEM) of the study area was developed from the Survey of
India(SOI) toposheet (1972) using Arcgis software. Watershed
delineation and derivation of required topographic parameters
for for calculating the peak discharge from different
watersheds were done with the help of the generated DEM.
Based on the hydrological analysis, means of safe diversion
of runoff water from hillocks was found out and canal
design of varying geometry capable of handling the peak
discharge suggested.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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 rainfall-runoff model for Chew and Kinder Reservoirs, Peak District; utilising the Flood Studies Report to find whether the dams at Chew and Kinder could withstand a 1-in-10,000 year storm (UK recommended safety limit)
Grade: 91%
Regional Rainfall Frequency Analysis By L-Moments Approach For Madina Region,...IJERDJOURNAL
ABSTRACT:- In arid regions, extreme rainfall event frequency predictions are still a challenging problem, because of the rain gauge stations scarcity and the record length limitation, which are usually short to insure reliable quantile estimates. Regional frequency analysis is one of the popular approaches used to compensate the data limitation. In this paper, regional frequency analysis of maximum daily rainfall is investigated for Madinah province in the Western Kingdom of Saudi Arabia (KSA). The observed maximum daily rainfall records of 20 rainfall stations are selected from 1968 to 2015. The rainfall data is evaluated using four tests, namely, Discordance test (Di), Homogeneity test (H), Goodness of fit test (Zdist) and L-moment ratios diagram (LMRD). The Di of L-moments shows that all the sites belong to one group (Di <3.0).><1). Finally, the Zdist is used to evaluate five probability distribution functions (PDFs) including generalized logistic (GLO), generalized extreme value (GEV), generalized normal (GNO), generalized Pareto (GPA), and Pearson Type III (PE3). Zdist and LMRD both showed that PE3 distribution is the best among the other PDFs. The regional parameters of the candidate PDF are computed using L-moments approach and accordingly the regional dimensionless growth curve is developed. The results enhance the accuracy of extreme rainfall prediction at-sites and also they can be used for ungauged catchment in the region.
A study confined to the lower tapi basin in Gujarat, India to find out the primary causes for 2006 floods in Surat city. The study involves collection of topographical data from the local geological survey organization, rainfall data from meteorological department of india and the application of HEC-HMS software from US Army corps of engineers to identify the primary cause of the runoff.
Presentation at the conference Greenmetrics 2016 of the paper "Geographical Load Balancing across Green Datacenters: a Mean Field Analysis" (authors G. Neglia, M. Sereno, G. Bianchi)
The Development of a Catchment Management Modelling System for the Googong Re...GavanThomas
A scenario assessment model to assist the end-user in determining priorities for a series of agreed management prescriptions that can be enacted through controls on existing landuse
Modelling of Short Duration Isopluvial Map For Raichur District Karnataka Moh...Mohammed Badiuddin Parvez
Everyoneacknowledges that it rains, runoff is generated for a design point of view we should know how much and how often it rains on our project location.Estimation of rainfall intensity is commonly required for the design of hydraulic and water resources engineering control structures. The present study aimed the Estimation of rainfall intensityin Raichur District using twenty five Rain gauge Station with 19 years of rainfall data (1998 to 2016). Log Normal Distribution, techniques are used to derived the rainfall intensity values of 2,5,10,15,30,60,120,720,1440 minutes of rainfall duration with different return period. The short duration IDF using daily rainfall data are presented, which is input for water resources projects. Isopluvial maps were developed for 25years, 50years, 75years and 100years return period
Hydrological Application of Remote – Sensing and GIS for Handling of Excess R...IDES Editor
A GIS based hydrological analysis has been carried
out to explore the possibility of diverting storm runoff
generated from the upper catchment safely through a canal
system constructed at the foothill to avoid flooding at
downstream. The study area consisted of Kalapahar-Udyachal
hills (5.38 km sq) in the Kahilipara- Odalbakra area, situated
in the city of Guwahati, Assam. The Digital Elevation Model
(DEM) of the study area was developed from the Survey of
India(SOI) toposheet (1972) using Arcgis software. Watershed
delineation and derivation of required topographic parameters
for for calculating the peak discharge from different
watersheds were done with the help of the generated DEM.
Based on the hydrological analysis, means of safe diversion
of runoff water from hillocks was found out and canal
design of varying geometry capable of handling the peak
discharge suggested.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hourly Groundwater Modelling In Tidal Lowlands Areas Using Extreme Learning M...IJERDJOURNAL
ABSTRACT:- The Information groundwater levels are very important in the management of tidal lowland, especially for food crop farming. This study aims to perform modelling groundwater levels using Extreme Learning Machine (ELM) paralleled with the Particle Swarm Optimization (PSO). PSO is used to set the value of the input weights and hidden biases on ELM methods in order to improve the performance of the method ELM. Groundwater levels are modelled is hourly groundwater level at tertiary block. Data input for modelling is the water level in the channel, rainfall and temperature. Results of ground water level predictions using ELMPSO is better than predictions of groundwater levels using ELM. Based on these results, the ELM-PSO can be used in predicting groundwater levels, so as to assist decision makers in determining water management strategies and the determination of appropriate cropping pattern in tidal lowland
As basic data, the reliability of precipitation data makes a significant impact on many results of environmental applications. In order to obtain spatially distributed precipitation data, measured points are interpolated. There are many spatial interpolation schemes, but none of them can perform best in all cases. So criteria of precision evaluation are established. This study aims to find an optimal interpolation scheme for rainfall in Ningxia. The study area is located in northwest China. Meteorological stations distribute at a low density here. Six interpolation methods have been tested after exploring data. Cross-validation was used as the criterion to evaluate the accuracy of various methods. The best results were obtained by cokriging with elevation as the second variable, while the inverse distance weighting (IDW) preform worst. Three types of model in cokriging were compared, and Gaussian model is the best.
Comparison of Ordinary Least Square Regression and Geographically Weighted Re...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
APPLICATION OF GENE EXPRESSION PROGRAMMING IN FLOOD FREQUENCY ANALYSISMohd Danish
Flood frequency and its magnitude are essential for the proper design of hydraulics structures such as bridges, spillways, culverts, waterways, roads, railways, flood control structures and urban drainage systems. Since, flood is a very complex natural event depending upon characteristics of catchment, rainfall conditions and various other factors, thus its analytical modelling is very difficult to pursue. Recently, artificial intelligence techniques such as gene expression programming (GEP), artificial neural network (ANN) etc. have been found to be efficient in modelling complex problems in hydraulic engineering. The performance of GEP model has been reported to be better than that of the ANN. Moreover, GEP provides mathematical equation which makes it more superior over other soft computing techniques that do not give any analytical mathematical equation. Therefore, in present study, GEP is implemented in flood frequency analysis for typical Indian river gauging station. The results obtained in the present study are highly promising and suggest that GEP modelling is a versatile technique and represents an improved alternative to the more conventional approach for the flood frequency analysis.
DELINEATION OF FLOOD-PRONE AREAS THROUGH THE PERSPECTIVE OF RIVER HYDRAULICSDasapta Erwin Irawan
Flash floods in the Saka River (part of the KUSW) struck Muara Dua District with a population of 177.47 people/km2 on May 8th, 2020, due to increased rainfall intensity and land cover changes upstream. Based on this incident, this research will examine hydraulic parameters that directly implications for potential flooding. The rainfall intensity analysis was based on calculations from the Gumbel-Sherman equation in the baseline period 2011-2020. Then the parameters of the runoff coefficient consisting of the slope, land cover, and type of lithology are analyzed by the Hassing method. The results of the rainfall intensity analysis showed that the lowest intensity occurred in August while the highest power occurred in November and April. The runoff coefficient of 53% has implications for peak flow discharge which has an average increase of 11.6%. Flood simulation in KUSW modeled with Hydrologic Engineering Center-River Analysis System (HEC-RAS) software shows 174.4 km2 potential flooding in the five years of the return period and 200 km2 in the ten years of the return period. This analysis model is used as a preventive effort and reduces the negative impact around KUSW.
Application of Support Vector Machine for River flow EstimationAI Publications
In recent years application of intelligent methods has been considered in forecasting hydrologic processes. In this research, month river discharge of kakareza, a river located in lorestan province at the west of Iran, was forecasted using Support vector machine and as genetic programming Inference System methods in dehno stations. In this regard, some different combinations in the period (1979-2015) as input data for estimation of discharge in the month index were evaluated. Criteria of correlation coefficient, root mean square error and Nash Sutcliff coefficient to evaluate and compare the performance of methods were used. It showed that combined structure by using surveyed inelegant methods, resulted to an acceptable estimation of discharge to the kakareza river. In addition comparison between models shows that Support vector machine has a better performance than other models in inflow estimation. In terms of accuracy, Support vector machine with correlation coefficients ( 0.970 ) has more propriety than root mean square error (0.08m3 /s ) and Nash Sutcliff ( 0.94 ) . To sum up, it is mentioned that Support vector machine method has a better capability to estimate the minimum, maximum and other flow values.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
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.
COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Application of Bayesian Regularized Neural Networks for Groundwater Level Modeling
1. Presented by Amir Mosavi
Application of Bayesian Regularized Neural
Networks for Groundwater Level Modeling
2. MATERIAL AND METHODS
Study area
The study area is the Mahabad plain which is located in the West Azarbaijan
Province, northwest of Iran. The plain is extended between longitudes of 45º 25ꞌ 19ꞌꞌ
and 45º 55ꞌ 30ꞌꞌ E and from latitudes of 36º 23ꞌ 20ꞌꞌ to 37º 1ꞌ 45ꞌꞌ N. Area of the plain is
about 1506.9 km2, which its aquifer has an area about
172.6 km2
Modeling process
In the current study, the Bayesian Regularized Neural Networks (BRNN) is used to
estimate the groundwater level
3. Results
Importance of the input variables
y = 0.9469x +0.1486
R² = 0.9494
1
2
3
4
5
1 2 3
GWL, BRNN
4 5
GWL,Observed
y = 0.8856x +0.2954
R² = 0.9098
1
2
3
4
5
1 2 3
GWL, BRNN
4 5
GWL,Observed
(b)Test
0 0.2 0.4 0.6 0.8 1
Importance
Groundwater level (t-1)
Outlet streamflow
Temperature
Evaporation
Precipitation
Scatter plot between observed and modeled
groundwater level (GWL): (a) train and (b) test
4. Application of Bayesian Regularized Neural
Networks for Groundwater Level Modeling
Bahram Choubin *
Soil Conservation and Watershed
Management Research Department
West Azarbaijan Agricultural and
Natural Resources Research and
Education Center, AREEO
Urmia, Iran
Farzaneh Sajedi Hosseini
Reclamation of Arid and Mountainous
Regions Department
Faculty of Natural Resources,
University of Tehran
Karaj, Iran
Amir Mosavi 1,2*
1Kando Kalman Faculty of Electrical
Engineering, Óbuda University
Budapest, Hungary
2 School of Economics and Business
Norwegian University of Life Sciences
1430 Ås, Norway
amir.mosavi@kvk.uni-obuda.hu
ZoltanFried
John von Neumann Faculty of
Informatics
Obuda University
Budapest, Hungary
Abstract— Current research uses a novel machine learning
method (i.e., Bayesian Regularized Neural Networks; BRNN) to
model the groundwater level (GWL) in the Mahabad Aquifer in
West Azarbaijan, Iran. Five exploratory factors including the
precipitation, evaporation, temperature, outlet streamflow, and
GWL (t-1) are considered as inputs to estimate the GWL (t) as
a response variable. The mean monthly of datasets for the
aquifer from April 2001 to March 2013 (i.e., 12 years) was
calculated using the Voronoi map in the ArcGIS based on the
data monitoring locations. A ratio of 70/30 was used for model
calibration and validation. Evaluation of the results indicated
that the model has an excellent performance in the GWL
modeling (RMSE = 0. 219; NSE= 0. 908; R-Squared = 0. 910).
Importance analysis of the variables indicated that the variables
of GWL (t-1), outlet streamflow, temperature, evaporation, and
precipitation respectively were the important variables and
have a higher contribution in groundwater level prediction.
Keywords—Bayesian regularized neural networks; machine
learning; groundwater; hydroinformatics; artificial intelligence;
earth system modeling
I. INTRODUCTION
The global groundwater resources hold approximately
one-fifth of the world’s freshwater supply [11, 12, 14, 22, 24].
Its total amount of freshwater makes up about the entire
planet’s frozen freshwater resources including ice sheets, ice
caps, glaciers, snow resources, ice packs, and icebergs [11, 15,
16, 17, 18, 26, 33]. Groundwater is extracted from the aquifers
under the land-surface where rocks, unconsolidated materials,
and soil are saturated with water. It is a cheaper, safer, and
more convenient reservoir of the natural water cycle [5, 34].
With less vulnerability to the surface pollutions and surface
droughts it had remained a reliable and important resource for
drinking and irrigation since the ancient era, and the early
explorations may date back to the first millennium BC [9, 10,
13, 19, 21, 27, 28, 29, 31, 32].
Population growth, increasing demand, and limited
surface water resources increase the need for groundwater
resources. Monitoring and accurate estimation of the
groundwater level are utmost of importance for managing the
water resources. Recently, the development of technology and
the emergence of artificial intelligence models have greatly
contributed to the study and prediction in the groundwater and
other related environmental fields. Previous studies have
applied artificial neural networks (ANN) [6], adaptive neuro-
fuzzy inference system (ANFIS) [3], support vector machine
(SVM) [25], extreme learning machines (ELM) [30], etc. for
groundwater level (GWL) prediction. However, advances in
the emergence of more powerful models can be a further aid
to the modeling process of these phenomena. The main
objective of this study was to estimate the GWL in the
Mahabad plain by application of a novel machine learning
model namely Bayesian Regularized Neural Networks
(BRNN) in this field.
II. MATERIAL AND METHODS
Study area
The study area is the Mahabad plain which is located in
the West Azarbaijan Province, northwest of Iran. The plain is
extended between longitudes of 45º 25ꞌ 19ꞌꞌ and 45º 55ꞌ 30ꞌꞌ E
and from latitudes of 36º 23ꞌ 20ꞌꞌ to 37º 1ꞌ 45ꞌꞌ N. Area of the
plain is about 1506.9 km2, which its aquifer has an area about
172.6 km2 (Fig. 1). The main structure of the plain is alluvial
deposits and fine-grained terraces. The thickness of the
groundwater aquifer is between 30 to 90 m [2]. Mahabad city
with 170,000 population is in this city. The main source of
agriculture and drinking water in this plain is groundwater.
The location of groundwater monitoring wells in this plain is
presented in Fig. 2.
Modeling process
In the current study, the Bayesian Regularized Neural
Networks (BRNN) is used to estimate the groundwater level
5. (GWL). The BRNN refers to a forward neural network based
on Bayesian regularization training. Regularization refers to
limiting the scale of thresholds and weights to improve the
generalization ability of the neural network [8]. The model has
good potential to handle and avoid overfitting problems [1].
Five exploratory factors including the precipitation,
evaporation, temperature, outlet streamflow, and GWL (t-1)
are considered as inputs to estimate the GWL (t) as a response
variable. Datasets were received from the Regional Water
Authority of the West Azarbaijan Province. At first, using the
Voronoi map in the ArcGIS the mean monthly GWL (t) for
the aquifer was calculated. Voronoi map is a partition of a
plane into regions close to points or sites, which is used to
calculate the weighted mean of a variable in an area [4, 7]. Fig.
2 shows the Voronoi map created based on 24 groundwater
monitoring wells.
Fig. 1. Studyarea
Also, the Voronoi map based on the weather stations (Fig.
1) was used for calculating the mean monthly values of
climate factors (i.e., precipitation, evaporation, and
temperature). Also, the streamflow in the outlet of the plain
was considered as input. It is noted that there is not a
hydrologic station for considering the streamflow in the inlet
of the plain. Moreover, the GLW from the previous month (t-
1) was considered to improve the modeling.
According to the data availability, datasets were from
April 2001 to March 2013 (i.e., 12 years) which as monthly
was used for the modeling process. From this period, 70 % of
data were randomly used for training the model and the rest
(30%) was applied for the validation. A k-fold cross-
validation methodology by the BRNN R package [23] was
conducted for running the BRNN model and estimating the
GWL. Three metrics of root mean square error (RMSE),
Nash–Sutcliffe efficiency (NSE) coefficient, and coefficient
of determination (R2) was used for evaluating the model
performance.
III. RESULTS AND DISCUSSION
In this study, after calculating the mean values of the
dataset for the aquifer, the modeling was conducted using the
BRNN model. The parameter of the model (i.e., number of
neurons) was optimized using the BRNN R package [23]. Fig.
2 shows the number of 2 neurons (with an RMSE equal to
0.177) was identified as the optimum number of neurons
among 1 to 10 based on the trial and error procedure. After
optimizing the model parameters, the GWL was estimated and
modeling results were evaluated. Results indicated that the
model have an excellent performance during the training
(RMSE = 0.165; NSE= 0.949; R2 = 0.949) and testing (RMSE
= 0. 219; NSE= 0. 908; R2 = 0. 910) phases (Table 1).
Fig. 3. Optimum number of neurons
The importance analysis of the variables indicated that the
variables of groundwater level (t-1), outlet streamflow,
temperature, evaporation, and precipitation respectively were
the important variables and have a higher contribution for
0.177
Fig. 2. Voronoi map to calculate the mean groundwater level for the aquifer
Fig. 4 shows the distribution of the observed values versus
the estimated values by the BRNN model. As can be seen, the
points are distributed around the 1:1 line which means a good
relationship between the observations and estimations.
0.20
0.19
0.18
0.16
0.17
1 2 3 4 5 6 7 8 9 10
Number of neurons
RMSE(m)
6. groundwater level prediction. In this regard, Choubin and
Malekian (2017) [6] indicated that the use of previous
groundwater level (t-1) improves the GWL modeling.
TABLE I. PERFORMANCE OF THE BRNN MODEL
Metric Train Test
RMSE 0.165 0.219
NSE 0.949 0.908
R2 0.949 0.910
Note: RMSE: Root Mean Square Error; NSE: Nash–
Sutcliffe efficiency; R2: Coefficient of Determination
(a) Train
Fig. 4. Scatter plot between observed and modeled groundwater level
(GWL): (a) train and (b) test
IV. CONCLUSION
Current research applied a novel machine learning model
namely the BRNN model for groundwater level modeling.
Modeling results indicated that the model have an excellent
performance in the GWL modeling (RMSE = 0. 219; NSE= 0.
908; R-Squared = 0. 910). Importance analysis of the input
factors showed that the variables of GWL (t-1), outlet
streamflow, temperature, evaporation, and precipitation
respectively were the important variables and have a higher
contribution in groundwater level prediction. Therefore, our
results highlighted that the BRNN model can successfully
predict the GWL and it can be valuable for water resource
managers within the data-scarce areas with low budgets for
monitoring objectives.
Fig. 5. Importance of the input variables
ACKNOWLEDGMENT
We acknowledge the financial support of this work by the
Hungarian-Mexican bilateral Scientific and Technological
(2019-2.1.11-TÉT-2019-00007) project. The support of the
Alexander von Humboldt Foundation is acknowledged. We
acknowledge the financial support of this work by the
Hungarian State and the European Union under the EFOP-
3.6.1-16-2016-00010 project and the 2017-1.3.1-VKE-2017-
00025 project. The research presented in this paper was
carried out as part of the EFOP-3.6.2-16-2017-00016 project
in the framework of the New Szechenyi Plan. The completion
of this project is funded by the European Union and co-
financed by the European Social Fund. We acknowledge the
financial support of this work by the Hungarian State and the
European Union under the EFOP-3.6.1-16-2016-00010
project. The support of the Alexander von Humboldt
Foundation is acknowledged.
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