V u surendran (53 résultats)

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Hardcover. Etat : New. Water is a valuable natural resource that is essential to all aspects of life on Mother "Earth" In fact, our planet is covered by 70% of water, but only 3% of the total water is in the form of freshwater, and again two-thirds of that is in the form of glaciers or is unavailable for our use. Presently, 1.1 billion people globally lack access to good drinking water resources, and another 2.7 billion find water scarce for at least one month of the year. With growing urban population and land use changes, water scarcity will be exacerbated, and also with climate change and bio-energy demands amplify the complex relationship between world development and water demand. The chapters cover the topic of: Introduction to Remote Sensing and Its Applications Quantitative Evaluation of Geomorphology Using Geo-Spatial Techniques Insights into the Groundwater Resource Mapping Assessing Spatial Changes in Blue and Green Water Resources GIS Tools for Mapping the Spatio-Temporal Trends in Rainfall Distribution Geographic Information System (GIS): A Practical Guide Towards Data Visualization, Analysis and Map Preparation.…

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Hardcover. Etat : 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.…

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