Prediction of factor of safety of slopes using stochastically modified ANN and classical methods: a rigorous statistical model selection approach

IF 3.3 3区工程技术 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Natural Hazards Pub Date : 2023-11-09 DOI:10.1007/s11069-023-06275-5

Abiodun Ismail Lawal, Shahab Hosseini, Minju Kim, Nafiu Olanrewaju Ogunsola, Sangki Kwon

引用次数: 0

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基于随机修正神经网络和经典方法的边坡安全系数预测:一种严格的统计模型选择方法

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Natural Hazards 环境科学-地球科学综合

CiteScore

6.60

自引率

8.10%

发文量

568

审稿时长

3.5 months

期刊介绍： Natural Hazards is devoted to original research work on all aspects of natural hazards, the forecasting of catastrophic events, their risk management, and the nature of precursors of natural and/or technological hazards. Although the origin of hazards can be different sources and systems (atmospheric, hydrologic, oceanographic, volcanologic, seismic, neotectonic), the environmental impacts are equally catastrophic. This circumstance warrants a tight interaction between the different scientific and operational disciplines, which should enhance the mitigation of hazards. Hazards of interest to the journal are included in the following sections: general, atmospheric, climatological, oceanographic, storm surges, tsunamis, floods, snow, avalanches, landslides, erosion, earthquakes, volcanoes, man-made, technological, and risk assessment. The interactions between these hazards and society are also addressed in the journal and include risk governance, disaster response and preventive actions such as spatial planning and remedial measures.