Titlebook: Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques; Hamid Reza Pourghasemi,Mauro Rossi Book 2019 Springer Nature Swit

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书目名称Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques读者反馈学科排名

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Gully Erosion Modeling Using GIS-Based Data Mining Techniques in Northern Iran: A Comparison BetweeMultivariate Adaptive Regression Spline (MARS) algorithms were implemented to model gully erosion susceptibility. Finally, Receiver Operating Characteristic (ROC) used for the assessment of prepared models. Based on the findings, BRT model (AUC = 0.894) had better efficiency than MARS model) AUC = 0

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Concepts for Improving Machine Learning Based Landslide Assessment,echniques), Sampling strategy (overcoming the overfit by choosing training instances wisely), Cross-scaling (a new concept for improving the algorithm’s learning capacity), Quasi-hazard concept (introducing artificial temporal base for upgrading from susceptibility to hazard assessment), and Objecti

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Assessment of the Contribution of Geo-environmental Factors to Flood Inundation in a Semi-arid Regip with 169 flood events was constructed through field surveys. These flood locations were then spatially randomly split into train, and validation sets with two different proportions of ratio 70 and 30%. Ten flood conditioning factors such as landuse, lithology, drainage density, distance from roads

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Application of Fuzzy Analytical Network Process Model for Analyzing the Gully Erosion Susceptibilitduced based on Fuzzy ANP weights and GIS aggregation functions. Results were validated by applying the known gullies collected in field surveys by GPS. The ROC curve was applied to investigate the susceptibility model’s performance. Results of the Fuzzy-ANP was revealed that drainage density, soil t

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Earthquake Events Modeling Using Multi-criteria Decision Analysis in Iran, earthquake events hazard. While, with increasing risk (no trade-off), all of the study area had earthquake events hazard. Low level of risk and no trade-off had the highest area in the very low class (98%), while high level of risk and average trade-off had the highest area in the very low class (1

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GIS-Based Landslide Susceptibility Evaluation Using Certainty Factor and Index of Entropy Ensembledre similar, slightly different results were obtained. IOE-ADTree was more practical, since it better predicts highly susceptible areas. The receiver operating characteristic (ROC) curve cleared further the differences so that IOE-ADTree with 84% fitting ability and 85.3% generalization capacity outp