Titlebook: Big Data, Artificial Intelligence, and Data Analytics in Climate Change Research; For Sustainable Deve Gaurav Tripathi,Achala Shakya,Pravee

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Polydipsia

https://doi.org/10.1007/978-3-031-01798-8USLE (rainfall erosivity, soil erodibility, slope length, slope steepness, land cover management, and conservation strategies). High-resolution satellite data of 5 m and DEM of 12.5 m were used to generate LULC and slope layer. Using GIS-based overlay analysis, all thematic layers have been analyzed

cultivated

Synthesis Lectures on Computer Scienceprofile. From 17.48 cubic meters in billion in 2006 to 3.28 cubic meters in billion in 2014, the yearly flow volume dropped. Following the construction of the Sarangkheda Dam, the yearly flow volume from pre-dam to post-dam averaged 5.93 cubic meters in billion across the downstream, and from 1985 t

引导

Introduction to Linear Elasticityspecies. The prediction methods play a vital role in accurate precipitation forecasts. This chapter aims to evaluate the accuracy of rainfall prediction in India through machine learning (ML) techniques like K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Ada-Boost (AB). The h

完成

Introduction to Linear Elasticityimplications for statistical tests and the generation of long-term data. Relying solely on assumptions proves inadequate for accurate analysis; thus, this study underscores the importance of collecting data through long-duration observations, harnessing the power of big data analytics. The spatial d

野蛮

Introduction and Mathematical Preliminaries,esult, governments are under pressure to create trustworthy and precise maps of flood risk regions and to further plan for sustainable flood risk management based on prevention, protection, and preparedness. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools in add

gerontocracy

诱拐

Extension, Bending and Torsion,n-depth overview of deep learning models specifically designed for fine-scale climate change prediction, with a primary focus on improving spatial and temporal resolution. The notion of deep learning and its applicability to studies on climate change are introduced at the beginning of the chapter. I

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纠缠,缠绕

https://doi.org/10.1007/978-3-030-52811-9ility based on various derived thematic maps like administrative boundaries with talukas, topography of the area, contour map, soil classification, geomorphology, and drainage pattern using the geoinformatics tools. In the present work, an attempt has been made to determine the spatial assessment of