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R Programming and Its Applications in Water Resources Management (Hardback, Naveena K, K Ch V Naga Kumar & Surendran U)

Brand: NIPA

Inclusive of all taxes

R Programming and Its Applications in Water Resources Management is a comprehensive hardback book authored by Naveena K, K Ch V Naga Kumar, and Surendran U, focusing on the application of R programming in tackling significant water resources challenges. It covers a broad spectrum of statistical techniques including linear and nonlinear modeling, time-series analyses, and machine learning algorithms. The book emphasizes the use of R packages tailored for water science issues, such as rainfall, droughts, and stream flow analysis. The first section introduces the basics of R programming specific to water resource management, followed by sections on climate and drought indices, statistical time series analysis, machine learning approaches, and hybrid models for advanced forecasting and resource management. This book not only serves as an advanced guide for researchers and practitioners in the field but also reflects the growing trend of R programming's integration into water science communities, highlighting its practical applications in real-world scenarios.

Key Features

Features Description
Comprehensive Coverage In-depth exploration of statistical techniques applied to water resource management.
Practical Applications Real-world applications of R programming to solve water-related issues.
Diverse Methodologies Incorporates advanced statistical models, machine learning techniques, and hybrid models.
Expert Contributions Contributions from leading experts in water resources management and R programming.
Hands-on Tools Guidance on data visualization tools and techniques specific to hydrological data analysis.
Attributes Description
Product Title R Programming and Its Applications in Water Resources Management
Authors Naveena K, K Ch V Naga Kumar, Surendran U
Format Hardback
Subject Water Resource Management, R Programming
Key Topics Statistical techniques, time-series analysis, machine learning, climate indices, drought assessment
Publication Year 2023
Pages Approx. 350 pages

Key Words

*Disclaimer: This above description has been AI generated and has not been audited or verified for accuracy. It is recommended to verify product details independently before making any purchasing decisions.

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Brand: NIPA

Country of Origin: India

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.
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 ResourcesNaveena 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



Books

NIPA books

R Programming

Water Resources Management

Statistical Analysis

Machine Learning

Drought Assessment

Hydrology

Climate Indices

Time Series Forecasting

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R Programming and Its Applications in Water Resources Management (Hardback, Naveena K, K Ch V Naga Kumar & Surendran U)

Brand: NIPA

Inclusive of all taxes

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