Inhaltsangabe
R Programming is a well-developed, simple, and effective programming language and an integrated environment for statistical computing and data analysis. It provides a wide variety of statistical techniques (e.g., linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and raster data processing. The growth of R usage in water science is reflected in the number of newly published packages connecting to water problems.
In this book, we explore the usage of different R packages to solve water science issues connecting to rainfall, drought, evapotranspiration, stream flow analysis, etc., advanced statistical and machine learning models like Modified Trend analysis, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), hybrid models (ARIMA-ANN, ARIMASVR), Multivariate modeling, spatiotemporal time series modeling, count time series modeling for forecasting, in addition to understanding the Remote Sensing and GIS applications toward mapping and understanding the changing Water Resources.
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Über die Autorin bzw. den Autor
Naveena K: Scientist, LWMRG, CWRDM completed his Ph.D.in Agricultural Statistics. He is a recipient of Young Achiever Award, Society for Advancement of Human and Nature (2015), SP Dhall Distinguished publication award in Statistics Society for Advancement of Human and Nature (2015) and Young Scientist award ASTHA Foundation 2015. He is a member of Indian Society of Agricultural Statistics (ISAS), Statistics Society for Advancement of Human and Nature (ASTHA) Foundation and Science and Tech Society for Integrated Rural Improvement. His research interest includesMachine Learning Modelling, Forecasting Modelling, Multivariate Analysis and Design of Experiment. He has more than 30 national and international publications.
K. Ch. V Naga Kumar: is working as Scientist at Information Systems Facility of LWMRG, CWRDM. He completed his Ph.D. in Geo-Engineering (Remote Sensing and GIS) and M.Tech in Remote Sensing and GIS. He is a recipient of ""Prof.K.Nageswara Rao Endowment Young Achiever Award-2016"" by Indian Society of Geomatics (ISG) and International Union for Quaternary Research (INQUA) Travel grant under Young Researcher category for attending INQUA Congress held at Nagoya, Japan during 26 July - 2 August, 2015.He is a member of Indian Society of Remote Sensing and Indian Society of Geomatics.His research interest includes Coastal Morphodynamics and Holocene Delta Evolution. He has more than 40 publications and handling different plan funded and externally funded projects of CWRDM.
Surendran U: Principal Scientist & Head, Land and Water Management Research Group (LWMRG), CWRDM, completed his Ph.D. and M.Sc. in Soil Science and Agricultural Chemistry. He is a recipient of Young Achiever award from SADHNA,Young scientist project by Department of Science and Technology (DST -SERC-Fast track). He is the editorial board member of Agricultural Water Management, Elsevier publishers, handling editor of Nature - Scientific reports, nature publishing group, and member - International Society for Agricultural Meteorologists, Netherlands etc.His research interest includes Decision Support systems for soil nutrient budgeting and its impact on crop yield, Adsorption of nutrient / Modeling nutrient behaviour in soils and its chemistry, Carbon Sequestration in soils, Climate change and its impact in agriculture, Crop Simulation Modelingetc. He has around 100 national and international publications. He handled different plan funded and external funded projects of CWRDM.
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Hardcover. Zustand: New. Contents: Section 1: Basics of R Programming and its Importance in Water Resource Analysis 1 An R-Based Approach Toward Understanding the Changing Dynamics in Water Resources Naveena K., Ch.V. Naga Kumar K., Surendran U., Santhosh Onte and Manoj P. Samuel 2 An Introduction to R Programming for Water Resource Management Naveena K., Santosha Rathod and Ch.V Naga Kumar 3 Data Visualization Tools and Techniques for Analyzing Hydrological Data in R Naveena K. and Ch.V Naga Kumar Section 2: Development of Climate and Drought Indices 4 Computation of Potential, Actual, and Reference Evapotranspiration Using R Package Evapotranspiration Fathima Sona N., Naveena K., and Surendran U. 5 Comparison of Meteorological Drought Indices for Assessment of Drought Features Santhosh Onte, Naveena K., Devika Sajan, Surendran U, and Ch.V Naga Kumar 6 Computation of Weather Indices for Climate Change Analysis Venu Prasad H.D. and Nandhana S. Section 3: Statistical Time Series Analysis for Water Resource Management 7 Data Uncertainties in Water Resources Modelling: A Special Reference to Hydrology Venu Prasad H.D.and Nandhana S. 8 Trend Analysis For Studying Different Climate Change Scenarios Connecting To Water Resources Naveena K., Surendran U., Drissia T.K. and Ch.V Naga Kumar 9 A Time Series Forecasting of Water Resources Using Autoregressive Integrated Moving Average (ARIMA) Models Naveena K. and Santosha Rathod 10 Modeling Time-Varying Volatility of Drought Using ARIMAGARCH. Naveena K., Halagundegowda G.R. and Nagaraja M.S. Section 4: Machine Learning Approach for Water Resource Management 11 A Comparative Study of Machine Learning Techniques for Forecasting Effective Drought Index Rajeev R.K., Mrinmoy R., Kanchan S.and Singh K.N. 12 Rainfall Prediction Using Neural Networks Veershetty, Harish Nayak G.H., G. Avinash, Vinay H.T. and Moumita Baishya 13 Multivariate Time Series Analysis for Prediction of RainfallA Machine Learning Approach Harish Nayak G.H., G. Avinash, Veershetty, Moumita Baishya and Vinay H.T. 14 Drought Coping Mechanisms: An Investigation of Determinants of Adoption by Decision Learning Approach Halagundegowda G.R., Singh A., Naveena K.and Nagaraja M.S. 15 Classification of Farmers Based on Drought Coping Strategies Using Support Vector Machine (SVM) Halagundegowda G.R., Singh A., Naveena K.and Nagaraja M.S. Section 5: Hybrid Time Series Modeling for Water Resource Management 16 Revolutionizing Rainfall Prediction: Boosting Accuracy with a Hybrid Statistical and Deep Learning Approach G. Avinash, Veershetty, Harish Nayak G.H., Vinay H.T. and Moumitha Baishya 17 Leveraging the Potential of Artificial Intelligence Techniques for Time Series Analysis: A Case Study of Modeling Water Stress Santosha Rathod, Amuktamalyada Gorlapalli and Naveena K. 18 A Two-Stage Modeling Framework for Time-Series Analysis of Spatiotemporal Data Santosha Rathod, Gayatri Chitikela, Amit Saha, Naveena K., Bishal Gurung, Mrinmoy Ray and K.N. Singh. Index. "R Programming' is a well-developed, simple, and effective programming language and an integrated environment for statistical computing and data analysis. It provides a wide variety of statistical techniques (e.g., linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and raster data processing. The growth of R's usage in water science is reflected in the number of newly published packages connecting to water problems. In this book, we explore the usage of different R packages to solve water science issues connecting to rainfall, drought, evapotranspiration, stream flow analysis, etc., advanced statistical and machine learning models like Modified Trend analysis, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), hybrid models (ARIMA-ANN, ARIMASVR), Multivariate modeling, spatiotemporal time series modeling, count time series modeling for forecasting, in addition to understanding the Remote Sensing. Artikel-Nr. 156183
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