LEADER 01008nam--2200349---450- 001 990002279700203316 005 20090218103151.0 035 $a000227970 035 $aUSA01000227970 035 $a(ALEPH)000227970USA01 035 $a000227970 100 $a20041217d1974----km-y0itay0103----ba 101 $aeng 102 $aGB 105 $a||||||||001yy 200 1 $aPublic expenditure economics$ean introductory application of welfare economics$fRobert Millward 210 $aLondon$cMcGrow-Hill$d1974 215 $aXII, 380 p.$d25 cm 410 0$12001 454 1$12001 461 1$1001-------$12001 676 $a332 700 1$aMILLAWARD,$bRobert$0570454 801 0$aIT$bsalbc$gISBD 912 $a990002279700203316 951 $a332 MIL 1 (IEP III 225)$b10360 E.C.$cIEP III$d00198294 959 $aBK 969 $aECO 979 $aSIAV7$b10$c20041217$lUSA01$h0919 979 $aRSIAV2$b90$c20090218$lUSA01$h1031 996 $aPublic expenditure economics$91067311 997 $aUNISA LEADER 06680nam 22007215 450 001 9910983358403321 005 20250202115225.0 010 $a9783031761898 010 $a3031761898 024 7 $a10.1007/978-3-031-76189-8 035 $a(CKB)37447302100041 035 $a(MiAaPQ)EBC31897088 035 $a(Au-PeEL)EBL31897088 035 $a(OCoLC)1496392393 035 $a(DE-He213)978-3-031-76189-8 035 $a(EXLCZ)9937447302100041 100 $a20250202d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced GIScience in Hydro-Geological Hazards $eApplications, Modelling and Management /$fedited by Md. Rejaur Rahman, Atiqur Rahman, S. K. Saha 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (0 pages) 225 1 $aGIScience and Geo-environmental Modelling,$x2730-7514 311 08$a9783031761881 311 08$a303176188X 327 $aGIScience and Earth observation Technology in Hydro-Geological hazard Analysis-An Overview -- Application of Advanced Geoinformation Science-A Disaster Risk Reduction Perspective -- New Paradigms of Decision Support Systems through Applications Leveraging Earth Observations and Machine Learning Approaches -- Soil erosion susceptibility modelling using machine learning in Guwahati urban watershed -- Flood vulnerability assessment in Jamuna (Brahmaputra) river basin, Bangladesh using remote sensing data, frequency ratio and machine learning based geospatial approach towards management strategies -- Introducing Autoencoder-Convolutional neural network for landslide susceptibility modelling in Kalimpong hill -- Artificial Neural Networks for combined forecasting of Tropical Cyclone track and intensity in the Bay of Bengal -- Geospatial approach to assess possible impact of groundwater abstraction in Bharatpur metropolitan city, Nepal -- Application of aerial photographs to monitor the dynamicsof fluvial tidal island and coastal erosion hazard in the Sundarbans delta plain of Bangladesh -- RS and GIS modelling for disaster management: Opportunities and challenges-A case study from Kerala, India -- Coastal Area Vulnerability to Cyclone Hazard-A Geoinformation and IPCC approach based study -- Spatial and Temporal Distribution of Drought in Bangladesh Using Novel China Z Index -- Livelihood vulnerability modelling with deep learning in erosion and flooding induced river islands in Ganga River corridor -- Assessment of soil erosion and landslide hazards in Rohingya refugee areas of Bangladesh: An integrated remote sensing and field observation approach -- Geospatial Analysis of River Bank Erosion-Accretion and Land Use Change of Teesta River, Bangladesh using Fuzzy ARTMAP Neural Network -- Landslides Vulnerability Assessment using Fuzzy logic based Spatial Multi-criteria Evaluation (Fuzzy-SMCE)-A Case Study from Hill Tract Districts, Bangladesh -- Impact of wetland transformation on fishing community in floodplain of Tangan River -- Multi-hazard risk assessment using Geo-statistical and Machine Learning Algorithm -- GNSS remote sensing in disaster management -- Land subsidence estimation using SAR time series data. 330 $aIn recent decades, natural hazards have increasingly threatened lives, livelihoods, and economies, with annual losses totalling billions of dollars globally. According to the Insurance Information Institute (III) and the Zebra, USA, natural disaster losses reached $74.4 billion in 2020, and an average of 6,800 natural disasters occur each year, claiming around 1.35 million lives. Hydrological and geological hazards, in particular, have significant societal and environmental impacts, making them critical areas of research. Understanding and mitigating these hazards is vital for developing legal mechanisms related to environmental restoration, societal improvements, and sustainable development. Modern technologies and earth observation data play a crucial role in disaster monitoring, prediction, modelling, and management. Recent advancements in geoinformation science have introduced multi-source data for natural hazards research. In addition, cutting-edge methods such as machine learning, deep learning, and big data science offer powerful tools for in-depth studies of natural hazards through remote sensing and geoinformatics. This book, Advanced GIScience in Hydro-Geological Hazards, presents up-to-date contributions on applying advanced GIScience to research various hydro-geological hazards, including floods, landslides, tropical cyclones, soil erosion, coastal erosion, riverbank erosion, coastal area vulnerability, drought, wetlands shrinking etc. It also explores multi-hazard studies using SAR, GNSS, and other innovative methods. The chapters focus on integrating artificial intelligence, machine learning techniques, and remote sensing to enhance preparedness, response, and resilience against these hazards. Targeting a broad audience of academics, scientists, students, environmentalists, government agencies, disaster planners, and GIS experts, this book aims to showcase the latest advancements in GIScience for assessing and managing hydro-geological hazards. It offers strategies for disaster risk reduction and capacity building, providing readers with the knowledge needed to address pressing environmental challenges. 410 0$aGIScience and Geo-environmental Modelling,$x2730-7514 606 $aGeographic information systems 606 $aNatural disasters 606 $aEcology 606 $aEnvironmental geography 606 $aArtificial intelligence 606 $aGeographical Information System 606 $aNatural Hazards 606 $aEnvironmental Sciences 606 $aIntegrated Geography 606 $aArtificial Intelligence 615 0$aGeographic information systems. 615 0$aNatural disasters. 615 0$aEcology. 615 0$aEnvironmental geography. 615 0$aArtificial intelligence. 615 14$aGeographical Information System. 615 24$aNatural Hazards. 615 24$aEnvironmental Sciences. 615 24$aIntegrated Geography. 615 24$aArtificial Intelligence. 676 $a910.285 700 $aRahman$b Rejaur$01786060 701 $aRahman$b Atiqur$01744479 701 $aSaha$b S. K$01786061 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983358403321 996 $aAdvanced GIScience in Hydro-Geological Hazards$94317475 997 $aUNINA