LEADER 03912nam 22005775 450 001 9911054599803321 005 20260112120432.0 010 $a3-032-11426-8 024 7 $a10.1007/978-3-032-11426-6 035 $a(CKB)44952011700041 035 $a(MiAaPQ)EBC32483436 035 $a(Au-PeEL)EBL32483436 035 $a(DE-He213)978-3-032-11426-6 035 $a(EXLCZ)9944952011700041 100 $a20260112d2026 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplication of Machine Learning in Earth Sciences $eA Practical Approach /$fedited by Swapnil Vyas, Shridhar D. Jawak, Pramit Kumar Deb Burman, Hemlata Patel, Avinash Kandekar, Suraj Sawant 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (815 pages) 225 1 $aEarth and Environmental Sciences Library,$x2730-6682 311 08$a3-032-11425-X 327 $aA ConvGRU Deep Learning Algorithm to Forecast global Ionospheric TEC Maps -- Estimation of Daily Air Relative Humidity Using a Novel Outlier-Robust Extreme Learning Machine Model: A Case Study of Two Algerian Locations -- Significance of Machine Learning in Understanding Earth?s Magnetosphere and Solar Activity -- Harnessing artificial intelligence for the detection and analysis of microplastics and associated chemicals in the atmosphere -- Application of Machine Learning in Bioremediation and Detection of Pollutants -- Machine Learning for Analysis of Water flow in the Reservoirs and Monitoring of Air quality -- Leveraging AI/ML for the Identification of Ma-rine Organisms -- Application of Machine Learning in River Water Quality Monitoring -- Application of AI/ML in river water quality monitoring -- Deep Neural Network for Water Mapping during Flood from SAR images using Matlab. 330 $aThis book introduces the reader to applications of machine learning (ML) in Earth Sciences. In detail, it describes the basic application of machine learning algorithms and models and their potential in Earth Sciences. It discusses the use of several tools and software and the typical workflow for ML applications in Earth Sciences. This book provides a comparative analysis of how standard processes and ML algorithms work in several Earth Sciences applications. Case studies from the various fields of Earth Sciences are presented to illustrate how to apply ML and Deep Learning, these include regression, forecasting, time series analysis in Climate studies, classification methods using multi-spectral data clustering, and dimensionality reduction in classification. This book reviews ML/AI models, algorithms, and methods, analyse case studies, and examine methods of application of ML/AI techniques to specific areas of Earth Sciences. It aims to serve all professionals, and researchers, scientists alike in academics, industries, government, and beyond. 410 0$aEarth and Environmental Sciences Library,$x2730-6682 606 $aEnvironmental sciences$xMathematics 606 $aEnvironmental monitoring 606 $aMachine learning 606 $aMathematical Applications in Environmental Science 606 $aEnvironmental Monitoring 606 $aMachine Learning 615 0$aEnvironmental sciences$xMathematics. 615 0$aEnvironmental monitoring. 615 0$aMachine learning. 615 14$aMathematical Applications in Environmental Science. 615 24$aEnvironmental Monitoring. 615 24$aMachine Learning. 676 $a333.7 700 $aVyas$b Swapnil$01889166 701 $aVyas$01744893 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911054599803321 996 $aApplication of Machine Learning in Earth Sciences$94529236 997 $aUNINA