UID:
edoccha_9961031986702883
Umfang:
1 online resource (578 pages)
ISBN:
0-323-91920-0
,
9780323918800
Serie:
Science of Sustainable Systems
Anmerkung:
Front Cover -- Water, Land, and Forest Susceptibility and Sustainability -- Science of Sustainable Systems: Water, Land, and Forest Susceptibility and Sustainability: Geospatial Approaches and Modeling Volume I -- Copyright -- Contents -- Contributors -- Foreword -- I - Introduction: Theoretical framework and -- 1 - Theoretical framework and approaches of susceptibility and sustainability: issues and drivers -- 1.1 Introduction -- 1.2 Global perspective of sustainability and susceptibility -- 1.3 Theoretical framework of sustainability and ecosystem services -- 1.3.1 Water, land, and forest: integral component of ecosystem -- 1.3.2 Sustainability and ecosystem services -- 1.3.3 Degradation of ecosystem services -- 1.4 Drivers and issues of susceptibility and sustainability of ecosystems -- 1.4.1 Susceptibility and sustainability of water resources -- 1.4.2 Susceptibility and sustainability of land resources -- 1.4.3 Susceptibility and sustainability of forest resources -- 1.5 Approaches for susceptibility/degradation assessment of ecosystems -- 1.6 Conclusions -- References -- II - Water resource susceptibility and sustainability -- 2 - Trap efficiency of reservoirs: concept, review, and application -- 2.1 Introduction -- 2.1.1 Reservoir sedimentation -- 2.1.2 Reservoir storage capacity reduction -- 2.1.3 Determination of quantity of sediment deposited in reservoir -- 2.1.3.1 Quantity of incoming sediment load -- 2.1.3.2 Quantity of outgoing sediment load -- 2.1.4 Determination of trap efficiency -- 2.2 Limitations of the study -- 2.2.1 Empirical methods -- 2.2.2 Artificial neural networks -- 2.3 Literature review -- 2.4 Materials and methods -- 2.4.1 Empirical methods -- 2.4.1.1 Brown's method -- 2.4.1.2 Capacity-inflow method (Brune method) -- 2.4.1.3 Gills method -- 2.4.1.4 Sediment index method (Churchill method).
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2.4.2 Artificial neural networks -- 2.4.2.1 Selection of ideal structure -- 2.4.2.2 Selection of input variables -- 2.4.2.3 Selection of hidden layers -- 2.4.2.4 Selection of hidden neurons -- 2.4.2.5 Development of ANN for trap efficiency assessment -- 2.5 Results and discussions -- 2.5.1 Analysis of results for different methods -- 2.5.2 Discussions of the results of empirical methods -- 2.6 Conclusions -- References -- 3 - GIS for Watershed Characterization and Modeling: example of the Taguenit River (Lakhssas, Morocco)∗∗Mohamed Abi ... -- 3.1 Introduction -- 3.2 Study area: geomorphology and hydro-climatology of the taguenit wadi watershed -- 3.3 Materials and methods -- 3.3.1 Flood mapping method -- 3.3.2 Factors use in FHI -- 3.3.3 Relative weight of factors -- 3.4 Results and discussion -- 3.5 Challenges and solutions -- 3.6 Recommendations -- 3.7 Conclusion -- References -- 4 - Application of artificial intelligence to estimate dispersion coefficient and pollution in river -- 4.1 Introduction -- 4.1.1 Objectives -- 4.2 Methodology -- 4.2.1 Data collection and analysis -- 4.2.1.1 Data collection -- 4.2.1.2 Data analysis -- 4.2.2 ANN modeling -- 4.2.2.1 Multilayer Perceptrons -- 4.2.3 Network architecture -- 4.2.4 Range of various parameters in the procured data -- 4.2.5 Data used for training and testing of ANN -- 4.2.6 Development of secondary parameters -- 4.3 Result and discussions -- 4.4 Models used for comparison -- 4.4.1 Comparison of models -- 4.5 Conclusion -- 4.6 Appendix 1 -- 4.7 Appendix 2 -- 4.8 Appendix 3 -- 4.9 Appendix 4 -- 4.10 Appendix 5 -- 4.11 Appendix 6 -- References -- 5 - Predicting nitrate concentration in river using advanced artificial intelligence techniques: extreme learning m ... -- 5.1 Introduction -- 5.2 Materials and methods -- 5.2.1 Study site -- 5.2.2 Performance assessment of the models -- 5.3 Methodology.
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5.3.1 Deep learning long short-term memory (LSTM) -- 5.3.2 Extreme learning machine (ELM) -- 5.3.3 Support vector regression (SVR) -- 5.3.4 Gaussian process regression (GPR) -- 5.4 Results and discussion -- 5.4.1 Daily time scale and scenario 01 for USGS 14211720 -- 5.4.2 Daily time scale and scenario 02 for USGS 14211720 -- 5.4.3 Hourly time scale and scenario 01 for USGS 14211720 -- 5.4.4 Hourly time scale and scenario 02 for USGS 14211720 -- 5.4.5 Discussion -- 5.5 Summary and conclusions -- References -- 6 - Polluted water bodies remediation by using GIS and remote sensing approach: a deeper insight -- 6.1 Introduction -- 6.1.1 Importance of remote sensing in water quality management and monitoring -- 6.1.2 Importance of GIS in water quality management and monitoring -- 6.2 Role of these advanced techniques in monitoring water quality -- 6.3 Remediation of surface water-using remote sensing and GIS -- 6.4 Future of these advanced techniques in monitoring the quality of water -- 6.5 Benefits of sensing in monitoring the quality of water -- 6.6 Conclusions -- References -- 7 - GIS-based spatial distribution analysis of water quality assessment using water pollution index of Yamuna river ... -- 7.1 Introduction -- 7.2 Study area -- 7.3 Materials and dataset -- 7.4 Methodology -- 7.4.1 Ground data -- 7.4.2 Remote-sensing data -- 7.4.2.1 Backscattering values -- 7.4.2.1.1 Raw data to dB conversion -- 7.4.2.1.1 Raw data to dB conversion -- 7.4.2.1.2 Backscattering values derivation -- 7.4.2.1.2 Backscattering values derivation -- 7.4.2.2 Chlorophyll Index -- 7.4.2.2.1 Preprocessing -- 7.4.2.2.1 Preprocessing -- 7.4.2.2.2 Chlorophyll Index calculation -- 7.4.2.2.2 Chlorophyll Index calculation -- 7.4.2.3 Normalized Coastal Aerosol Index -- 7.4.2.4 Derive land surface temperature -- 7.4.2.4.1 Top of atmospheric spectral radiance correction.
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7.4.2.4.1 Top of atmospheric spectral radiance correction -- 7.4.2.4.2 Conversion of radiance to at sensor temperature -- 7.4.2.4.2 Conversion of radiance to at sensor temperature -- 7.4.2.4.3 NDVI method for emissivity correction -- 7.4.2.4.3 NDVI method for emissivity correction -- 7.4.2.4.3.1 Land surface emissivity calculation -- 7.4.2.4.3.1 Land surface emissivity calculation -- 7.4.2.5 Water pollution index -- 7.5 Results -- 7.5.1 Backscattering SAR data values -- 7.5.1.1 Comparative analysis of premonsoon season in 2019 and 2020 for backscattering values -- 7.5.1.2 Comparative analysis of monsoon season in 2019 and 2020 for backscattering values -- 7.5.1.3 Comparative analysis of postmonsoon season in 2019 and 2020 for backscattering values -- 7.5.2 Chlorophyll Index -- 7.5.2.1 Comparative analysis of premonsoon season in 2019 and 2020 for Chlorophyll Index -- 7.5.2.2 Comparative analysis of postmonsoon season in 2019 and 2020 for Chlorophyll Index -- 7.5.3 Land surface temperature -- 7.5.3.1 Comparative analysis of premonsoon season in 2019 and 2020 for land surface temperature -- 7.5.3.2 Comparative analysis of postmonsoon season in 2019 and 2020 for land surface temperature -- 7.5.4 Normalized Coastal Aerosol Index -- 7.5.4.1 Comparative analysis of premonsoon season in 2019 and 2020 for NCAI -- 7.5.4.2 Comparative analysis of postmonsoon season in 2019 and 2020 for NCAI -- 7.5.5 Water pollution index -- 7.5.5.1 Comparative analysis of the premonsoon season in 2019 and 2020 using the WPI model -- 7.5.5.2 Comparative analysis of the postmonsoon season in 2019 and 2020 using the WPI model -- 7.6 Validation -- 7.7 Discussion -- 7.8 Conclusion -- References -- 8 - Groundwater sustainability: role of monitoring, modeling, and management -- 8.1 Introduction -- 8.2 Limitations of study -- 8.3 Methodology -- 8.3.1 Study basin.
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8.3.2 Data monitoring and field campaigns -- 8.4 Methods -- 8.4.1 Groundwater flow simulation modeling -- 8.4.1.1 Boundary conditions -- 8.4.1.2 Initial condition -- 8.4.1.3 Calibration and validation -- 8.4.2 Decision support system -- 8.5 Results -- 8.5.1 Groundwater recharge -- 8.5.2 Pumping well boundary condition -- 8.5.3 River boundary conditions -- 8.5.4 Groundwater flow simulation -- 8.5.5 Decision support system -- 8.6 Conclusion -- References -- Further reading -- III - Land resources susceptibility and sustainability -- 9 - Landslide susceptibility modeling using a generalized linear model in a tropical river basin of the Southern We ... -- 9.1 Introduction -- 9.2 Study area -- 9.3 Materials and methods -- 9.3.1 Landslide inventory and spatial database -- 9.3.2 Lithology -- 9.3.3 Soil texture -- 9.3.4 Land use/land cover -- 9.3.5 Slope angle -- 9.3.6 Normalized difference vegetation index -- 9.3.7 Slope aspect -- 9.3.8 Distance from road -- 9.3.9 Distance from Lineaments -- 9.3.10 Topographic wetness index -- 9.3.11 Terrain roughness index -- 9.3.12 Distance from stream -- 9.3.13 Stream power index -- 9.3.14 Profile curvature -- 9.3.15 Planform curvature -- 9.3.16 Selection of conditioning factors -- 9.3.17 Multicollinearity analysis -- 9.3.18 Generalized linear modeling -- 9.3.19 Model validation -- 9.4 Results -- 9.4.1 Multicollinearity and variable importance -- 9.4.2 Landslide susceptibility modeling -- 9.4.3 Model validation -- 9.4.4 Landslide susceptibility map (LSM) -- 9.5 Discussion -- 9.6 Summary and conclusion -- References -- Further reading -- 10 - Assessment of the land use and landcover changes using remote sensing and GIS techniques -- 10.1 Introduction -- 10.1.1 Impact of land use and landcover changes in a basin -- 10.1.2 Effect of land use and landcover changes on ecosystem services in a basin.
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10.1.3 Land use and land cover change identification using remote sensing and geographical information Systems.
Weitere Ausg.:
Print version: Chatterjee, Uday Water, Land, and Forest Susceptibility and Sustainability San Diego : Elsevier,c2022
Sprache:
Englisch
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