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首页> 外文期刊>ISPRS International Journal of Geo-Information >An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method
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An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method

机译：基于GIS和随机森林法的同震滑坡敏感性综合模型

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The Mw 7.8 Gorkha earthquake of 25 April 2015 triggered thousands of landslides in the central part of the Nepal Himalayas. The main goal of this study was to generate an ensemble-based map of co-seismic landslide susceptibility in Sindhupalchowk District using model comparison and combination strands. A total of 2194 co-seismic landslides were identified and were randomly split into 1536 (~70%), to train data for establishing the model, and the remaining 658 (~30%) for the validation of the model. Frequency ratio, evidential belief function, and weight of evidence methods were applied and compared using 11 different causative factors (peak ground acceleration, epicenter proximity, fault proximity, geology, elevation, slope, plan curvature, internal relief, drainage proximity, stream power index, and topographic wetness index) to prepare the landslide susceptibility map. An ensemble of random forest was then used to overcome the various prediction limitations of the individual models. The success rates and prediction capabilities were critically compared using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). By synthesizing the results of the various models into a single score, the ensemble model improved accuracy and provided considerably more realistic prediction capacities (91%) than the frequency ratio (81.2%), evidential belief function (83.5%) methods, and weight of evidence (80.1%).
机译：2015年4月25日发生的7.8级Gorkha地震在尼泊尔喜马拉雅山中部引发了数千起滑坡。这项研究的主要目标是使用模型比较和组合链生成基于集合的信德帕帕乔克地区同震滑坡敏感性图。共确定了2194个同震滑坡，并随机分为1536个（约70％），以训练用于建立模型的数据，其余658个（约30％），用于模型的验证。应用频率比，证据置信函数和证据权重方法，并使用11种不同的成因进行了比较（峰值地面加速度，震中邻近度，断层邻近度，地质学，高程，坡度，平面曲率，内部起伏，排水邻近度，水流功率指数，以及地形湿度指数）来绘制滑坡敏感性图。然后使用一组随机森林来克服各个模型的各种预测限制。使用接收器工作特性曲线（ROC）的曲线下面积（AUC）严格比较成功率和预测能力。通过将各种模型的结果综合为一个分数，集成模型提高了准确性，并提供了比频率比（81.2％），证据置信函数（83.5％）和权重大得多的现实预测能力（91％）。证据（80.1％）。

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