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首页> 外文期刊>Natural Hazards >Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy
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Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy

机译：Logistic回归与人工神经网络的比较：意大利Serchio河谷采样区的滑坡敏感性评估

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This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instabilitydeveloped through statistical models (conditional analysis and logistic regression), and neural network application, in order to better understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence,and to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslidehazard in the area of the Sheet 250 "Castelnuovo di Garfagnana" (1:50,000 scale). The study area is located in the middle part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological, and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means of indirect-quantitative statistical methods and neural network software application. Experimental results from different methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic (ROC) curves analysis approach.
机译：本文通过逻辑回归，人工神经网络和地理信息系统（GIS）技术，提出了一种多学科的滑坡敏感性制图方法。通过统计模型（条件分析和逻辑回归）以及神经网络的应用，开发了用于对边坡失稳进行分级的方法，以便更好地了解地质/地貌地貌与过程与滑坡发生之间的关系，并提高滑坡敏感性。楷模。拟议的实验研究涉及由托斯卡纳地区管理局和APAT-意大利地质调查局推动的一项广泛的研究项目，旨在确定250号图纸“ Castelnuovo di Garfagnana”（1：50,000比例）地区的滑坡灾害。该研究区位于Serchio流域的中部，由于其地质，地貌和气候特征，其滑坡敏感性高，在意大利最为严重。已经通过间接定量统计方法和神经网络软件应用来解决地形对边坡破坏的敏感性。提出并讨论了来自不同方法的实验结果以及这种方法的潜力和陷阱。应用多元统计分析可以更好地理解这种现象，并量化不稳定因素与滑坡发生之间的关系。特别是，配备有监督学习和错误控制功能的多层神经网络的应用提高了模型的性能。最后，已经通过接收机工作特性（ROC）曲线分析方法进行了评估多元模型分类效率的首次尝试。

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