Hybrid method for rainfall-induced regional landslide susceptibility mapping

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-09-18 DOI:10.1007/s00477-024-02753-9

Shuangyi Wu, Huaan Wang, Jie Zhang, Haijun Qin

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Abstract

Landslide susceptibility maps can provide important information for managing regional landslide risks. Traditionally, data-driven and physically-based models are widely used for rainfall-induced landslide susceptibility mapping, but each method has limitations. In this study, a hybrid method that integrates a data-driven model and a physically-based model is proposed for rainfall-induced landslide susceptibility mapping, where the uncertainty in the soil properties can be explicitly considered. The proposed method is illustrated with landslide susceptibility mapping in Shengzhou County, Zhejiang Province, China. Logistic regression is used as the data-driven model, and the regional assessment of rainfall-induced landslides model (RARIL) is used as the physically-based model. Three hybrid models are developed. Hybrid model I, which considers soil parameters uncertainty, is compared with hybrid models II and III, which do not consider it. Results indicate that all the three hybrid models outperform the conventional logistic regression and RARIL models. Notably, hybrid model I, which considers the soil parameters uncertainty, outperforms hybrid models II and III, which do not consider it.

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降雨诱发区域滑坡易发性绘图的混合方法

滑坡易发性地图可为管理区域滑坡风险提供重要信息。传统上，数据驱动模型和基于物理的模型被广泛应用于降雨诱发的滑坡易感性绘图，但每种方法都有其局限性。本研究提出了一种将数据驱动模型和物理模型相结合的混合方法，用于绘制降雨诱发的滑坡易发性图谱，其中明确考虑了土壤特性的不确定性。以中国浙江省嵊州市的滑坡易发性测绘为例说明了所提出的方法。数据驱动模型采用逻辑回归，物理模型采用降雨诱发滑坡区域评估模型（RARIL）。建立了三个混合模型。考虑了土壤参数不确定性的混合模型 I 与不考虑土壤参数不确定性的混合模型 II 和 III 进行了比较。结果表明，所有三个混合模型都优于传统的逻辑回归模型和 RARIL 模型。值得注意的是，考虑了土壤参数不确定性的混合模型 I 优于未考虑该不确定性的混合模型 II 和 III。

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来源期刊

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

CiteScore

7.10

自引率

9.50%

发文量

189

审稿时长

3.8 months

期刊介绍： Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.