Geomorphic Risk Reduction Using Geospatial Methods and Tools

• Autore: Sarkar Raju
• Pubblicazione: Singapore : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (328 pages)
• Disciplina: 363.3472
• Altri autori: SahaSunil AdhikariBasanta Raj ShawRajib
• Nota di contenuto: Intro -- Preface -- About This Book -- Contents -- Editors and Contributors -- Part I Geomorphic Hazards and Machine Learning Techniques -- 1 Landslide Susceptibility Assessment Based on Machine Learning Techniques -- 1.1 Introduction -- 1.1.1 Landslides -- 1.1.2 Development of Landslide Susceptibility Assessment -- 1.1.3 Machine Learning for Dealing with Regression and Classification Problems -- 1.1.4 Supervised and Unsupervised Learning -- 1.2 Landslide Inventory and Disaster-Related Geo-Environmental Variable Dataset -- 1.2.1 Landslide Inventory -- 1.2.2 Geo-Environmental Database -- 1.3 Machine Learning Methods for Landslide Susceptibility Assessment -- 1.3.1 Linear Regression -- 1.3.2 Logistic Regression -- 1.3.3 Naïve Bayes -- 1.3.4 K-Nearest Neighbors -- 1.3.5 K-Means Clustering -- 1.3.6 Random Forest -- 1.3.7 Boosting Algorithms -- 1.3.8 Support Vector Machine -- 1.3.9 Artificial Neural Network -- 1.4 Summarization -- 1.4.1 Scale -- 1.4.2 Performance -- 1.4.3 Modeling -- 1.4.4 Interpretability -- References -- 2 Gully Erosion Susceptibility Using Advanced Machine Learning Method in Pathro River Basin, India -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.2.1 Study Area -- 2.2.2 Preparing Gully Erosion Influencing Factors -- 2.2.3 Multicollinearity Test Among Independent Factors -- 2.2.4 Gully Erosion Susceptibility Mapping Based on BRT Model -- 2.2.5 Performance Assessment Through Receiver Operating Characteristics (ROC) Curve -- 2.3 Results -- 2.3.1 Considering Multicollinearity Effective Factors -- 2.3.2 Gully Erosion Susceptibility Model (GESM) Based on BRT -- 2.3.3 Validation of BRT Model -- 2.4 Discussion and Conclusion -- References -- 3 Artificial Neural Network Ensemble with General Linear Model for Modeling the Landslide Susceptibility in Mirik Region of West Bengal, India -- 3.1 Introduction -- 3.2 Study Area. ; 3.3 Materials and Methods -- 3.3.1 Landslide Inventory Map (LIM) -- 3.3.2 Preparing Landslide Conditioning Factors -- 3.3.3 Multicollinearity Test -- 3.3.4 Methods of Landslide Susceptibility Mapping -- 3.3.5 Validation Methods -- 3.4 Results -- 3.4.1 Multicollinearity Analysis -- 3.4.2 Landslide Susceptibility Maps -- 3.4.3 Analysis of Factor Importance by Frequency Ratio (FR) -- 3.4.4 Validation and Comparison of Models -- 3.5 Discussion -- 3.6 Conclusion -- References -- 4 An Advanced Hybrid Machine Learning Technique for Assessing the Susceptibility to Landslides in the Upper Meenachil River Basin of Kerala, India -- 4.1 Introduction -- 4.2 Study Area -- 4.3 Material and Method -- 4.3.1 Methodology -- 4.3.2 Landslide Inventory Map (LIM) -- 4.3.3 Preparing Landslide Conditioning Factors (LCF) -- 4.3.4 Multi-Collinearity Test -- 4.3.5 Methods of Landslide Susceptibility Mapping -- 4.3.6 Validation Methods -- 4.4 Results -- 4.4.1 Multi-Collinearity Analysis -- 4.4.2 Landslide Susceptibility Maps (LSMs) -- 4.4.3 Analysis of Factor Importance by Information Gain Ratio (IGR) -- 4.4.4 Validation and Comparison of Models -- 4.5 Discussion -- 4.6 Conclusion -- References -- 5 Novel Ensemble of M5P and Deep Learning Neural Network for Predicting Landslide Susceptibility: A Cross-Validation Approach -- 5.1 Introduction -- 5.2 Study Area -- 5.3 Material and Method -- 5.3.1 Methodology -- 5.3.2 Landslide Inventory Map (LIM) and Cross-Validation Method -- 5.3.3 Preparing Landslide Conditioning Factors -- 5.3.4 Multi-collinearity Test -- 5.3.5 Methods of Landslide Susceptibility Mapping -- 5.3.6 Validation Methods -- 5.4 Results -- 5.4.1 Multi-collinearity Analysis -- 5.4.2 Landslide Susceptibility Maps -- 5.4.3 Analysis of Factor Importance by Random Forest (RF) -- 5.4.4 Validation and Comparison of Models -- 5.5 Discussion -- 5.6 Conclusion -- References. ; 6 Assessment of Landslide Vulnerability Using Statistical and Machine Learning Methods in Bageshwar District of Uttarakhand, India -- 6.1 Introduction -- 6.2 Study Area -- 6.3 Material and Methods -- 6.3.1 Landslide Inventory -- 6.3.2 Landslide Vulnerability Determining Factors: -- 6.3.3 The Logistic Regression (LR): -- 6.3.4 Artificial Neuron Networks (ANN) -- 6.3.5 Validation Method-ROC Curve -- 6.4 Results -- 6.4.1 Landslide Susceptibility (Lsi) -- 6.4.2 Socio-Economic Vulnerability (S−evi) -- 6.4.3 Relative Landslide Vulnerability (RLvi) -- 6.4.4 Validation and Comparison: -- 6.5 Discussion -- 6.6 Conclusion -- References -- 7 An Ensemble of J48 Decision Tree with AdaBoost and Bagging for Flood Susceptibility Mapping in the Sundarbans of West Bengal, India -- 7.1 Introduction -- 7.2 Study Area -- 7.3 Material and Methods -- 7.3.1 Study Design -- 7.3.2 Data Source -- 7.3.3 Preparation of Flood Inventory Map (FIM) -- 7.3.4 Determination of Flood Conditioning Factors (FCFs) -- 7.3.5 Multi-collinearity Analysis -- 7.3.6 Models Applied for Flood Susceptibility Mapping -- 7.4 Results -- 7.4.1 Analysis of Multi-collinearity Problem Among FCFs -- 7.4.2 Assessment of Flood Susceptibility -- 7.4.3 Factors Importance Analysis -- 7.4.4 Validation and Comparison -- 7.5 Discussion -- 7.6 Conclusion -- References -- 8 Spatial Flash Flood Modeling in the Beas River Basin of Himachal Pradesh, India, Using GIS-Based Machine Learning Algorithms -- 8.1 Introduction -- 8.2 Study Area -- 8.3 Material and Method -- 8.3.1 Methodology -- 8.3.2 Flash Flood Inventory Map (FFIM) -- 8.3.3 Preparing Flash Flood Conditioning Factors -- 8.3.4 Multi-Collinearity Test -- 8.3.5 Methods of Flash Flood Susceptibility Mapping -- 8.3.6 Validation Methods -- 8.4 Results -- 8.4.1 Multi-collinearity Analysis -- 8.4.2 Flash Flood Susceptibility Maps. ; 8.4.3 Analysis of Factor Importance by Random Forest (RF) -- 8.4.4 Validation and Comparison of Models -- 8.5 Discussion -- 8.6 Conclusion -- References -- Part II Geomorphic Hazards and Multi-temporal Satellite Images -- 9 Quantitative Assessment of Interferometric Synthetic Aperture Radar (INSAR) for Landslide Monitoring and Mitigation -- 9.1 Introduction -- 9.1.1 Description of the El Forn Landslide -- 9.1.2 Physics-Based Model -- 9.2 Materials and Methods -- 9.2.1 Experimental Design -- 9.3 Results -- 9.3.1 Correlating InSAR with In-Situ Data for Seasonal and Extreme Ground Motion -- 9.3.2 Ordinary Kriging: Assessing the Necessary Number of InSAR Observations -- 9.4 Discussion -- References -- 10 Geospatial Study of River Shifting and Erosion-Deposition Phenomenon Along a Selected Stretch of River Damodar, West Bengal, India -- 10.1 Introduction -- 10.2 Study Area -- 10.3 Methodology -- 10.3.1 Data Used and Methodology -- 10.3.2 Morphometric Analysis -- 10.3.3 Sinuosity Index -- 10.3.4 Braiding Index -- 10.3.5 Alpha Index -- 10.4 Statistical Analysis -- 10.4.1 Correlation Coefficient -- 10.4.2 Student's t-test -- 10.4.3 River Reaches and Cross-Sections -- 10.5 Results and Discussion -- 10.5.1 Temporal Changes in Morphometric Parameters -- 10.5.2 Statistical Interpretation of Various Morphometric Parameters -- 10.5.3 Erosion-Deposition and River Shifting Phenomena -- 10.5.4 Spatio-Temporal Variation of Erosion-Deposition Scenarios Along the Selected River Stretch -- 10.5.5 Reach-Wise Erosion and Deposition Phenomena and Shifting of River Course from 2001 to 2018 -- 10.5.6 Statistical Interpretation of Erosion and Deposition Study -- 10.6 Conclusions -- References -- 11 Assessing the Shifting of the River Ganga Along Malda District of West Bengal, India Using Temporal Satellite Images -- 11.1 Introduction -- 11.2 Study Area. ; 11.3 Materials and Methods -- 11.3.1 Method of Image Classification and Accuracy Assessment -- 11.4 Results and Discussion -- 11.4.1 Shifting of Channel -- 11.4.2 Impact of Channel Shifting on Land Use and Land Cover Along River Bank 1990-2020 -- 11.5 Conclusion -- References -- 12 An Evaluation of Hydrological Modeling Using CN Method and Satellite Images in Ungauged Barsa River Basin of Pasakha, Bhutan -- 12.1 Introduction -- 12.2 Study Area -- 12.2.1 Study Method -- 12.2.2 Soil Grouping: Infiltration Testing -- 12.2.3 Hydrologic Soil Group (HSG) -- 12.2.4 Curve Number -- 12.3 Runoff Simulation in HEC-HMS 3.4 -- 12.4 Conclusion -- References -- 13 Measuring Landslide Susceptibility in Jakholi Region of Garhwal Himalaya Using Landsat Images and Ensembles of Statistical and Machine Learning Algorithms -- 13.1 Introduction -- 13.2 Study Area -- 13.3 Materials and Methods -- 13.3.1 Preparing Landslide Inventory Map -- 13.3.2 Preparing Landslide Conditioning Factors -- 13.3.3 Multicollinearity Test -- 13.3.4 Certainty Factor (CF) Model -- 13.3.5 Support Vector Machine (SVM) -- 13.3.6 Random Forest -- 13.3.7 ANN -- 13.3.8 Model Validation Techniques -- 13.4 Results -- 13.4.1 Analyzing the Multicollinearity -- 13.4.2 Relationship Between Landslides and Conditioning Factors -- 13.4.3 Landslide Susceptibility Condition -- 13.4.4 Model Validation and Comparison -- 13.5 Discussion -- 13.6 Conclusion -- References -- 14 Landslide Susceptibility Mapping Using Satellite Images and GIS-Based Statistical Approaches in Part of Kullu District, Himachal Pradesh, India -- 14.1 Introduction -- 14.2 Research Area -- 14.3 Methodology -- 14.4 Data Used and Database Preparation -- 14.4.1 Landslide Inventory -- 14.4.2 Prepared Thematic Layers -- 14.5 Adopted Probabilistic Approaches -- 14.5.1 Frequency Ratio (FR) -- 14.5.2 Shannon Entropy (SE). ; 14.5.3 Information Value (IV).
• Titolo autorizzato: Geomorphic Risk Reduction Using Geospatial Methods and Tools
• ISBN: 981-9977-07-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910855372203321
• Lo trovi qui: Univ. Federico II
• Serie: Disaster Risk Reduction Series