Landslide : Application of Geostatistical and Geospatial Modeling

• Autore: Panda Gopal Krishna
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (722 pages)
• Altri autori: ShawRajib PalSubodh Chandra ChatterjeeUday SahaAsish
• Nota di contenuto: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- Part I Landslide Hazard Susceptibility -- 1 Landslide Risk and Vulnerability -- Real Issues, Thoughts and Perspectives -- 1.1 Introduction -- 1.2 Rational of the Study -- 1.3 Materials and Methods -- 1.4 Results and Discussion -- 1.4.1 Geological Characteristics of Ghana -- 1.4.2 Precipitation Characteristics of Ghana -- 1.4.3 Land Cover and Land Use Characteristics of Ghana -- 1.5 Risk Assessment -- 1.5.1 Influence of Physical Factors on Landslide Risk in Ghana -- 1.6 Vulnerability Assessment -- 1.7 Limitations of Study -- 1.8 Recommendation -- 1.9 Conclusion -- References -- 2 Landslide Susceptibility Mapping by Using Geospatial Technique: Reference from Hofu City, Yamaguchi Prefecture, Japan -- 2.1 Introduction -- 2.2 Database and Methodology -- 2.2.1 Study Area -- 2.2.2 Dataset -- 2.2.3 Method -- 2.3 Result -- 2.3.1 Dem -- 2.3.2 Geological Map -- 2.3.3 The Spatial Distribution of Land Cover -- 2.3.4 Rainfall Map -- 2.3.5 The LSM -- 2.4 Discussion -- 2.5 Conclusion -- 2.6 Author Contribution Statement -- References -- 3 Landslide Hazard Risk and Vulnerability Monitoring-GIS Based Approach -- 3.1 Introduction -- 3.1.1 Understanding Landslide as a Potential Hazard -- 3.1.2 Potential Impact of Landslide -- 3.1.3 Most Effected Geographical Regions -- 3.1.4 Understanding Landslide Hazard Risk and Vulnerability Monitoring -- 3.1.5 Importance of Landslide Vulnerability Monitoring -- 3.2 What Landslide Risk Is and How It Monitored? -- 3.3 Importance of Considering Landslide Vulnerability and Exposure in Risk Assessment -- 3.3.1 Defining Vulnerability -- 3.3.2 Types of Vulnerability -- 3.3.3 Defining Exposure -- 3.3.4 Factors to Be Considered Under Estimation of Exposure -- 3.4 Identification of Landslides as Hazard. ; 3.4.1 Preparation of Inventory of Historical Events -- 3.4.2 Factors Responsible for the Occurance of Landslide -- 3.5 Different Approaches Used for Evaluation of Conditioning Factors as an Important Factor in Causing Landslide Hazard -- 3.6 Review on Statistical and Machine Learning Techniques Used for Landslide Hazard Susceptibility Mapping -- 3.7 Application of Remote Sensing (RS) and Geographic Information System (GIS) in Landslide Hazard Risk and Vulnerability Monitoring -- 3.7.1 Data Collection and Preparation -- 3.7.2 Spatial Analysis for Data Processing and Standardization -- 3.7.3 Weightage Overlay Analysis and Integration of Data -- 3.7.4 Validation, Calibration and Accuracy Assessment -- 3.7.5 Real Time Updation, Visualization and Map Designing -- 3.8 Applicability of Landslide Hazard Risk and Vulnerability Monitoring -- 3.9 Summary -- References -- 4 Landslide Susceptibility Mapping Methods-A Review -- 4.1 Introduction -- 4.2 Failure Mechanism of Slopes -- 4.3 Landslide Inventory -- 4.4 Landslide Susceptibility, Hazard and Risk-An overview -- 4.5 Physically Based Landslide Models (PBLM) -- 4.6 Landslide Susceptibility using Statistical Methods -- 4.7 Heuristic Methods -- 4.8 Conclusion -- References -- 5 Mapping of Landslide Susceptibility Using State-of-the-Art Method and Geospatial Techniques in the Rangamati District in the Chattogram Hill Tracts Region of Bangladesh -- 5.1 Introduction -- 5.2 Materials and Methods -- 5.2.1 Study Area -- 5.2.2 Landslide Location Identification -- 5.2.3 Preparation Landslide Causative Factors Datasets -- 5.2.4 Data Processing and Analysis -- 5.3 Results and Discussion -- 5.3.1 Landslide Inventory -- 5.3.2 Association of Landslides with Different Causative Factors -- 5.4 Conclusion -- References -- 6 Towards Artificially Intelligent Landslide Susceptibility Mapping: A Critical Review and Open Questions. ; 6.1 Introduction -- 6.2 Previous Literature Reviews -- 6.3 Theoretical Background -- 6.3.1 Feature Selection -- 6.3.2 Machine Learning (ML) -- 6.3.3 Deep Learning (DL) -- 6.3.4 Hyperparameter Optimization -- 6.4 Materials and Methods -- 6.5 Results and Discussion -- 6.5.1 Geographical Distribution -- 6.5.2 Landslide Conditioning Factors -- 6.5.3 Susceptibility Modeling Approaches -- 6.5.4 Feature Selection for Optimal Subset -- 6.5.5 Hyperparameter Optimization Algorithms -- 6.6 Open Questions and Challenges -- 6.7 Review Summary and Conclusions -- References -- 7 Landslide Susceptibility Analysis by Frequency Ratio Model and Analytical Hierarchical Process in Mirik and Kurseong, Darjeeling Himalaya, India -- 7.1 Introduction -- 7.2 Study Area -- 7.3 Materials and Method -- 7.3.1 Landslide Experimentary Factor -- 7.3.2 Methods for Landslide Susceptibility Zonation (LSZ) -- 7.3.3 Landslide Inventories -- 7.3.4 Validation of Model's Performance -- 7.4 Result and Discussion -- 7.5 Conclusion -- References -- 8 Suitability Analysis of Landslide Susceptibility Model of Uttarkashi District in Uttarakhand, India: A Comparative Approach Between Weighted Overlay and Multi-criteria Decision Analysis -- 8.1 Introduction -- 8.2 Methodology -- 8.2.1 Weighted Overlay Technique -- 8.2.2 Multi Criteria Decision Analysis -- 8.3 Results and Discussions -- 8.3.1 A Brief Idea About the Study Area -- 8.3.2 Analysis of Conditioning Factors Used for Modelling -- 8.3.3 Landslide Susceptibility Analysis of Uttarkashi -- 8.3.4 Comparative Analysis of the Areal Coverage of Susceptibility Zones -- 8.3.5 Model Validation -- 8.4 Conclusion -- References -- 9 Determining Land Induced Factors for Landslide Susceptibility in Indian Cities -- 9.1 Introduction -- 9.1.1 Landslide as Emerging Issue -- 9.2 Paradox of Land Use -- Land Cover and Occurrence of Landslide. ; 9.3 Definition Understanding the Land Use and Land Cover -- 9.3.1 Land Use -- 9.3.2 Land Cover -- 9.4 Identifying the Relevant Parameters that Impact Landslide Occurrence -- 9.4.1 Factors Affecting the Occurrence of Landslides -- 9.5 The Impact of Land Use on Landslide Susceptibility -- 9.5.1 Dichotomous Urbanization and Increasing Landslide -- 9.5.2 Impact of Agricultural Land Holding on Landslide Occurrence -- 9.5.3 Impact of Deforestation on Landslide Occurrence -- 9.5.4 Illegal Mining Activities Trigger the Landslide Occurrence -- 9.6 Factors Impacting Landslide Susceptibility -- 9.6.1 Key Concerns for Landslide Susceptibility -- 9.7 The Role of Geotechnical Characteristics in Landslide Susceptibility -- 9.8 Case Studies and Research Findings -- 9.8.1 Case Study 1: Land Use Change and Landslide Occurrence in India -- 9.8.2 Case Study 2: Vegetation Cover and Slope Stability -- 9.8.3 Case Study 3: Mining Activities and Landslide Risk -- 9.9 Mitigation Strategies and Best Practices -- 9.10 Conclusion -- Reference -- 10 Moisture-Driven Landslides and Cascade Hazards in the Himalayan Region: A Synthesis on Predictive Assessment -- 10.1 Introduction -- 10.2 Rainfall-Induced Landslides: Mechanisms, Triggers, and Complex Interplay -- 10.3 Determining Moisture Thresholds for Landslides -- 10.4 Multi-Hazard Risk Framework for Moisture-Driven Landslides -- 10.5 Moisture-Driven Landslides in Anthropocene across High Mountain Asia -- 10.6 Discussion and Conclusions -- References -- 11 Landslide Susceptibility Map Showing the Spatial Relationship Between Various Landslide Factors and Landslide Using Remote Sensing and GIS-Based Frequency Ratio Method in Chamoli District, Uttarakhand, India -- 11.1 Introduction -- 11.2 Database and Methodology -- 11.2.1 Objective of the Study -- 11.2.2 Study Area -- 11.2.3 History of Landslides -- 11.3 Methodology. ; 11.3.1 Selection of Landslide Conditioning Parameters -- 11.3.2 Data Collection and Organization -- 11.3.3 Construction of a Landslide Inventory Map -- 11.3.4 Construction of Various Thematic Maps of Landslide Conditioning Parameters -- 11.4 Result and Discussion -- 11.4.1 LSZ in the Study Region -- 11.5 Accuracy Assessment of the Landslide Susceptibility Map (LSM) -- 11.6 Conclusion -- References -- 12 Landslide Susceptibility Using Weighted Regression Model: A Geo-spatial Approach -- 12.1 Introduction -- 12.2 Study Area -- 12.3 Data -- 12.4 Methodology -- 12.5 Discussion -- 12.6 Result -- 12.6.1 Lineament -- 12.6.2 NDVI -- 12.6.3 Slope -- 12.6.4 Aspect -- 12.6.5 Rainfall -- 12.6.6 Lithology -- 12.6.7 Soil -- 12.6.8 Soil Depth -- 12.6.9 Land Use Land Cover -- 12.7 Landslide Susceptibility -- 12.8 Limitation -- 12.9 Conclusion -- 12.9.1 Future Scope -- References -- 13 Assessment of Climate Change Impact on Landslides in Darjeeling District of West Bengal: A Geospatial, Geostatistical and Ecosystem Service Based Approach -- 13.1 Introduction -- 13.2 Materials and Methods -- 13.2.1 Study Area -- 13.2.2 Road Contributing Area -- 13.2.3 Frequency Ratio Modeling -- 13.2.4 Precipitation Concentration Index (PCI) -- 13.2.5 LST Mapping -- 13.2.6 SEVI -- 13.2.7 Assignment of Ecosystem Service Valuation (ESV) -- 13.3 Results and Discussions -- 13.4 Conclusion -- References -- 14 Landslide Vulnerability Analysis of Tourist Spots Through Shannon Entropy Model: A Case Study on Rudraprayag, Uttarakhand -- 14.1 Introduction -- 14.2 Methodology -- 14.2.1 Conditioning Factors -- 14.2.2 Database -- 14.2.3 Shannon Entropy Model -- 14.2.4 Calculation of Landslide Susceptibility Value of Different Religious Tourist Spots -- 14.2.5 Processing and Analysis -- 14.3 Results and Discussions -- 14.3.1 Analysis of the Conditioning Factors Affecting Landslides. ; 14.3.2 Landslide Susceptibility Assessment.
• Titolo autorizzato: Landslide
• ISBN: 3-031-56591-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910861097003321
• Lo trovi qui: Univ. Federico II
• Serie: Advances in Natural and Technological Hazards Research Series