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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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發(fā)表于 2025-3-21 19:58:45 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques
編輯Hamid Reza Pourghasemi,Mauro Rossi
視頻videohttp://file.papertrans.cn/662/661756/661756.mp4
概述Offers useful studies on geo-spatial modelling for optimal land use management and planning.Discusses with concrete examples the use of data mining algorithms for spatial modeling of natural hazards i
叢書名稱Advances in Natural and Technological Hazards Research
圖書封面Titlebook: Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques;  Hamid Reza Pourghasemi,Mauro Rossi Book 2019 Springer Nature Swit
描述This edited volume assesses capabilities of data mining algorithms for spatial modeling of natural hazards in different countries based on a collection of essays written by experts in the field. The book is organized on different hazards including landslides, flood, forest fire, land subsidence, earthquake, and gully erosion. Chapters were peer-reviewed by recognized scholars in the field of natural hazards research. Each chapter provides an overview on the topic, methods applied, and discusses examples used. The concepts and methods are explained at a level that allows undergraduates to understand and other readers learn through examples. This edited volume is shaped and structured to provide the reader with a comprehensive overview of all covered topics. It serves as a reference for researchers from different fields including land surveying, remote sensing, cartography, GIS, geophysics, geology, natural resources, and geography. It also serves as a guide for researchers, students, organizations, and decision makers active in land use planning and hazard management.
出版日期Book 2019
關(guān)鍵詞Landslide susceptibility, hazard, and risk assessment; Flash flood modeling and zonation; Data mining
版次1
doihttps://doi.org/10.1007/978-3-319-73383-8
isbn_ebook978-3-319-73383-8Series ISSN 1878-9897 Series E-ISSN 2213-6959
issn_series 1878-9897
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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發(fā)表于 2025-3-21 22:07:29 | 只看該作者
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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發(fā)表于 2025-3-22 02:52:25 | 只看該作者
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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發(fā)表于 2025-3-22 18:32:19 | 只看該作者
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
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