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R Programming and Its Applications in Water Resources Management
R Programming and Its Applications in Water Resources Management
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Author : Naveena K, K Ch V Naga Kumar & Surendran U
Pages : 250
Year of Publication : 2025
Language : English
Binding : Hardback
“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 and GIS applications toward mapping and understanding the changing Water Resources.
Pages : 250
Year of Publication : 2025
Language : English
Binding : Hardback
“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 and GIS applications toward mapping and understanding the changing Water Resources.
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