The document presents a case study using multiple linear regression to predict monthly rainfall amounts in various regions of India between 1905-2015. Rainfall data from 36 meteorological subdivisions was analyzed. Missing data was imputed using predictive mean matching. Regression models were developed to predict rainfall for each subdivision from June to December based on previous months, and residuals between predicted and actual values were calculated. The mean absolute difference between predictions and actuals was around 75-76% overall, with higher errors for regions of higher average rainfall that experience more inter-annual variability.
Time Series Least Square Forecasting Analysis and Evaluation for Natural Gas ...rahulmonikasharma
Power and mechanical energies are now a down main stream of energy to produce any other type of product in any nation in the world. Currently natural resources are the main stream of resources to produce the power energy or mechanical energy. We aware that the natural resource energy is limited.It’s the prime focus now a day to optimize the natural resource consumption. Natural Gas is an integral part of the natural resource and hence needs a planning and forecasting to optimize the natural gas usage and wastage. Without the proper planning and forecasting, the natural gas consumption will be highly usage and wasted and hence the limited resource will be drained off. In this paper, we analyze the time series least square method and compare its results with the actual data with absolute error percentage. This paper provides a clear analytic results and comparisons which serves as a base forecasting model for natural gas consumption.
This paper discusses a new algorithm of a univariate method, which is vitally important to develop a short-term load forecasting module for planning and operation of distribution system. It has many applications including purchasing of energy, generation and infrastructure development etc. We have discussed different time series forecasting approaches in this paper. But ARIMA has proved itself as the most appropriate method in forecasting of the load profile for West Bengal using the historical data of the year of 2017. Auto Regressive Integrated Moving Average model gives more accuracy level of load forecast than any other techniques. Mean Absolute Percentage Error (MAPE) has been calculated for the mentioned forecasted model.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
Predicting Agricultural Products Volume With APC ERP DataQUESTJOURNAL
ABSTRACT: The purpose of the study is to predict agricultural product volumes using Agricultural product processing center (APC) enterprise resource planning (ERP) data from Korea. So far, great attention by the government has been shown to predict agricultural product volumes to stabilize price, especially high volatility products such as cabbage. In the past, it was hard to predict volumes precisely due to the lack of useful data. Recently, useful data has been accumulated from various sources such as sensors in greenhouses, information systems and public areas. This makes it possible to predict agricultural product volumes more precisely. For this study, we employ the support vector machine (SVM) to predict cabbage volumes. SVM is a semiparametric technique with origins in the machine-learning literature of computer science and its prediction performance is well known. We explore results using SVM against three other methods: ordinary least square (OLS), auto regression (AR), and vector auto regression (VAR). The results show that the prediction performance of SVM is better than that of the other three methods. We expect that the results can be applied to predict domestic cabbage volumes and ERP dashboard for top management at the APC.
Time Series Least Square Forecasting Analysis and Evaluation for Natural Gas ...rahulmonikasharma
Power and mechanical energies are now a down main stream of energy to produce any other type of product in any nation in the world. Currently natural resources are the main stream of resources to produce the power energy or mechanical energy. We aware that the natural resource energy is limited.It’s the prime focus now a day to optimize the natural resource consumption. Natural Gas is an integral part of the natural resource and hence needs a planning and forecasting to optimize the natural gas usage and wastage. Without the proper planning and forecasting, the natural gas consumption will be highly usage and wasted and hence the limited resource will be drained off. In this paper, we analyze the time series least square method and compare its results with the actual data with absolute error percentage. This paper provides a clear analytic results and comparisons which serves as a base forecasting model for natural gas consumption.
This paper discusses a new algorithm of a univariate method, which is vitally important to develop a short-term load forecasting module for planning and operation of distribution system. It has many applications including purchasing of energy, generation and infrastructure development etc. We have discussed different time series forecasting approaches in this paper. But ARIMA has proved itself as the most appropriate method in forecasting of the load profile for West Bengal using the historical data of the year of 2017. Auto Regressive Integrated Moving Average model gives more accuracy level of load forecast than any other techniques. Mean Absolute Percentage Error (MAPE) has been calculated for the mentioned forecasted model.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
Predicting Agricultural Products Volume With APC ERP DataQUESTJOURNAL
ABSTRACT: The purpose of the study is to predict agricultural product volumes using Agricultural product processing center (APC) enterprise resource planning (ERP) data from Korea. So far, great attention by the government has been shown to predict agricultural product volumes to stabilize price, especially high volatility products such as cabbage. In the past, it was hard to predict volumes precisely due to the lack of useful data. Recently, useful data has been accumulated from various sources such as sensors in greenhouses, information systems and public areas. This makes it possible to predict agricultural product volumes more precisely. For this study, we employ the support vector machine (SVM) to predict cabbage volumes. SVM is a semiparametric technique with origins in the machine-learning literature of computer science and its prediction performance is well known. We explore results using SVM against three other methods: ordinary least square (OLS), auto regression (AR), and vector auto regression (VAR). The results show that the prediction performance of SVM is better than that of the other three methods. We expect that the results can be applied to predict domestic cabbage volumes and ERP dashboard for top management at the APC.
Multiple imputation for hydrological missing data by using a regression metho...eSAT Journals
Abstract Rainfall amounts and water surface elevation are considered as one of the most important climatic parameters. Because these two parameters will have a direct impact on water resources management decisions such as meet the water needs and prevent flooding. But in some cases, for some reason all time series data are not fully recorded. To fill the gaps in the data, several interpolation methods currently used. One of these methods is regression analysis as a statistical method. By using regression, we can determine the mathematical relationship coefficients between inputs and outputs. By achieving the equation, we can obtain the unknown quantities. In this research, the daily data between 2005 to 2015 for 5 Rain-gauge stations and 3 elevation measurement of water surface stations in the Klang River Basin were used. The main goal was to find the missing value of the water level in the mentioned three stations by rainfall and water level data. To evaluate the obtained results, Multiple R, R2, and Standard Error were used. The results indicate that the standard error in normalized data was less than the regular data. Multiple r values for the Klang at Taman Sri Muda1, Klang at Jam, Sulaiman, WP and Klang at Emp Genting Klang, WP are 0.35, 0.42 and 0.28, respectively. Keywords: Filling gaps, Interpolation, Data mining, Statistical method, Rainfall, Water level
Data Warehousing and Business Intelligence Project on Smart Agriculture and M...Kaushik Rajan
Implemented a Data Warehouse on smart agriculture to solve various Business Intelligence queries. Integrated multiple datasets from 3 different data sources including both structured and unstructured data.
Tools used:
> SQL Server Integration Services for ETL
> SQL Server Management Services for Database
> SQL Server Analysis Services for building the Schema
> Tableau and PowerBI for Visualization
> R for data preprocessing
> LATEX for documentation
Video Presentation: https://www.youtube.com/watch?v=0oIlLQcyPdM
Analysis of Crime Big Data using MapReduceKaushik Rajan
Analyzed Crime Big data of Washington DC to solve the following business queries:
> Which hour has the highest crime count?
> Which shift has the highest crime count?
> Year wise crime count
> Hour wise crime count
> Crime count by an offense
> Average of Shift wise crime count
The data was initially stored in MySql which was then moved to HDFS using SQOOP, from where 4 MapReduce operations are doing using JAVA in Eclipse IDE. The outputs of the queries are then moved to HBase using SQOOP. Two more MapReduce operations are done using PIG, the output of which is also moved to HBase using SQOOP. All the outputs were then moved to the local system and are visualized using RStudio and Tableau.
Tools used:
> MySQL, HDFS and HBase to store the data
> SCOOP to move the data from one database to another
> JAVA (Eclipse IDE) and PIG to run the MapReduce queries
> RStudio for data pre-processing and visualization
> Tableau for visualization
> LATEX for Documentation
Business Analytics Foundation with R Tools - Part 3Beamsync
Beamsync is top training institute for "business analytics" course in Bangalore. If you are looking for classroom training for analytics course visit: http://beamsync.com/business-analytics-training-bangalore/
Multiple imputation for hydrological missing data by using a regression metho...eSAT Journals
Abstract Rainfall amounts and water surface elevation are considered as one of the most important climatic parameters. Because these two parameters will have a direct impact on water resources management decisions such as meet the water needs and prevent flooding. But in some cases, for some reason all time series data are not fully recorded. To fill the gaps in the data, several interpolation methods currently used. One of these methods is regression analysis as a statistical method. By using regression, we can determine the mathematical relationship coefficients between inputs and outputs. By achieving the equation, we can obtain the unknown quantities. In this research, the daily data between 2005 to 2015 for 5 Rain-gauge stations and 3 elevation measurement of water surface stations in the Klang River Basin were used. The main goal was to find the missing value of the water level in the mentioned three stations by rainfall and water level data. To evaluate the obtained results, Multiple R, R2, and Standard Error were used. The results indicate that the standard error in normalized data was less than the regular data. Multiple r values for the Klang at Taman Sri Muda1, Klang at Jam, Sulaiman, WP and Klang at Emp Genting Klang, WP are 0.35, 0.42 and 0.28, respectively. Keywords: Filling gaps, Interpolation, Data mining, Statistical method, Rainfall, Water level
Data Warehousing and Business Intelligence Project on Smart Agriculture and M...Kaushik Rajan
Implemented a Data Warehouse on smart agriculture to solve various Business Intelligence queries. Integrated multiple datasets from 3 different data sources including both structured and unstructured data.
Tools used:
> SQL Server Integration Services for ETL
> SQL Server Management Services for Database
> SQL Server Analysis Services for building the Schema
> Tableau and PowerBI for Visualization
> R for data preprocessing
> LATEX for documentation
Video Presentation: https://www.youtube.com/watch?v=0oIlLQcyPdM
Analysis of Crime Big Data using MapReduceKaushik Rajan
Analyzed Crime Big data of Washington DC to solve the following business queries:
> Which hour has the highest crime count?
> Which shift has the highest crime count?
> Year wise crime count
> Hour wise crime count
> Crime count by an offense
> Average of Shift wise crime count
The data was initially stored in MySql which was then moved to HDFS using SQOOP, from where 4 MapReduce operations are doing using JAVA in Eclipse IDE. The outputs of the queries are then moved to HBase using SQOOP. Two more MapReduce operations are done using PIG, the output of which is also moved to HBase using SQOOP. All the outputs were then moved to the local system and are visualized using RStudio and Tableau.
Tools used:
> MySQL, HDFS and HBase to store the data
> SCOOP to move the data from one database to another
> JAVA (Eclipse IDE) and PIG to run the MapReduce queries
> RStudio for data pre-processing and visualization
> Tableau for visualization
> LATEX for Documentation
Business Analytics Foundation with R Tools - Part 3Beamsync
Beamsync is top training institute for "business analytics" course in Bangalore. If you are looking for classroom training for analytics course visit: http://beamsync.com/business-analytics-training-bangalore/
Linear Regressions of Predicting Rainfall over Kalay Regionijtsrd
Regression analysis is a statistical technique for investigating the relationship between variables. In this paper, rainfall and water level prediction models are discussed with the use of empirical statistical technique, Simple Linear Regression and analyzed the development of the predictive power of Linear Regression model to forecast the predicting rainfall and water level over Kalay in Sagaing Region for 10 years 2008 2017 .The data of the monthly rainfall and water level used in this study were obtained from Meteorology and Hydrology Department of Kalay, Myanmar. In July 2015, Kalay was affected by the floods. So the rainfall and water level are predicted for next five years in this paper. Ohnmar Myint "Linear Regressions of Predicting Rainfall over Kalay Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26711.pdf Paper URL: https://www.ijtsrd.com/mathemetics/computational-science/26711/linear-regressions-of-predicting-rainfall-over-kalay-region/ohnmar-myint
Data Analysis for Solar Energy Generation in a University Microgrid IJECEIAES
This paper presents a data acquisition process for solar energy generation and then analyzes the dynamics of its data stream, mainly employing open software solutions such as Python, MySQL, and R. For the sequence of hourly power generations during the period from January 2016 to March 2017, a variety of queries are issued to obtain the number of valid reports as well as the average, maximum, and total amount of electricity generation in 7 solar panels. The query result on all-time, monthly, and daily basis has found that the panel-by-panel difference is not so significant in a university-scale microgrid, the maximum gap being 7.1% even in the exceptional case. In addition, for the time series of daily energy generations, we develop a neural network-based trace and prediction model. Due to the time lagging effect in forecasting, the average prediction error for the next hours or days reaches 27.6%. The data stream is still being accumulated and the accuracy will be enhanced by more intensive machine learning.
MFBLP Method Forecast for Regional Load Demand SystemCSCJournals
Load forecast plays an important role in planning and operation of a power system. The accuracy of the forecast value is necessary for economically efficient operation and also for effective control. This paper describes a method of modified forward backward linear predictor (MFBLP) for solving the regional load demand of New South Wales (NSW), Australia. The method is designed and simulated based on the actual load data of New South Wales, Australia. The accuracy of discussed method is obtained and comparison with previous methods is also reported.
Gensol collected Actual Global Tilted Irradiation (AGTI) of 57 sites from operational projects spread across in India. It was then correlated with Expected Global Tilted Irradiation (EGTI) from the following meteo-databases namely:
1) Meteonorm-7.2
2) SolarGIS,
3) NASA (National Aeronautics and Space Administration),
4) NREL (National Renewable Energy Laboratory)
In our report, we find most representative meteo-data set for each site.
Similar to Rainfall Forecasting : A Regression Case Study (20)
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
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TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.