“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.

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 Rainfall—A 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