Department of Geography and Regional Research, University of Vienna, Vienna 1010, Austria.
National Technical University of Athens, School of Mining and Metallurgical Engineering, Department of Geological Sciences, Laboratory of Engineering Geology and Hydrogeology, Zografou Campus: Heroon Polytechniou 9, 15780 Zografou, Greece.
Sci Total Environ. 2020 Nov 10;742:140549. doi: 10.1016/j.scitotenv.2020.140549. Epub 2020 Jul 3.
The main objective of the current study was to present a methodological approach that combines Information Theory, a neural network and meta-heuristic techniques so as to generate a landslide susceptibility map. Specifically, the methodology involved three important tasks: Classifying the landslide related variables, weighting them and optimizing the structural parameters of the neural network. Shannon's entropy index was used to estimate for each landslide related variable the number of classes which maximized the information coefficient, whereas the Certainty Factor method was used to weight the variables. A Neural Network, a (NN) which uses stochastic gradient descent (SGD), the structural parameters of which are optimized by a Genetic Algorithm (GA), was implemented to generate the landslide susceptibility map. A well defined spatial database which included 380 landslides and fourteen related variables (elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index, stream power index, stream transport index, land use cover, distance to road, distance to faults, distance to river, lithology and soil cover) were considered for implementing the NN-SGD-GA model, in the Yanshan County located in Shangrao Municipality, in the north-eastern of Jiangxi province, China. To validate the predictive power of the novel model, a Logistic Regression (LR) and Random Forest (RF) model were used for comparison. The results showed that the NN-SGD-GA model achieved the highest prediction accuracy (88.10%), followed by the RF (86.26%) and the LR (85.82%) models. Furthermore, by analyzing the validation data, concerning the spatial distribution of landslides and the susceptibility index, the proposed model showed an area under curve value of 0.8212, followed by the RF (0.8124) and the LR (0.8020) models. Finally, the proposed model showed the highest relative landslide density value of 65.09, followed by the RF (62.51) and the LR (61.76) models, when using the validation dataset. The novelty of our approach is the usage of an intelligent way to select and classify the most appropriate prognostic variables and also the implementation of an evolutionary wrapper automatic procedure that efficiently generates prediction models with reduced complexity and adequate generalization capacity. Overall, the proposed model can be successfully used for landslide susceptibility mapping as an alternative spatial investigation tool.
本研究的主要目的是提出一种结合信息论、神经网络和启发式技术的方法，以生成滑坡易发性图。具体来说，该方法涉及三个重要任务：对滑坡相关变量进行分类、加权和优化神经网络的结构参数。Shannon 熵指数用于估计每个与滑坡相关的变量最大化信息系数的类别数，而确定性因子方法用于加权变量。实现滑坡易发性图的是一个使用随机梯度下降（SGD）的神经网络（NN），其结构参数通过遗传算法（GA）进行优化。为了实施 NN-SGD-GA 模型，考虑了一个定义明确的空间数据库，其中包括 380 个滑坡和 14 个相关变量（海拔、坡度、方位、平面曲率、剖面曲率、地形湿度指数、溪流功率指数、溪流输运指数、土地利用覆盖、道路距离、断层距离、河流距离、岩性和土壤覆盖）。在中国江西省东北部上饶市的延山县实施了 NN-SGD-GA 模型。为了验证新型模型的预测能力，使用逻辑回归（LR）和随机森林（RF）模型进行比较。结果表明，NN-SGD-GA 模型的预测精度最高（88.10%），其次是 RF（86.26%）和 LR（85.82%）模型。此外，通过分析验证数据，关于滑坡的空间分布和易发性指数，所提出的模型的曲线下面积值为 0.8212，其次是 RF（0.8124）和 LR（0.8020）模型。最后，当使用验证数据集时，所提出的模型的相对滑坡密度值最高为 65.09，其次是 RF（62.51）和 LR（61.76）模型。我们方法的新颖之处在于使用智能方式选择和分类最合适的预测变量，并实施一种有效的进化包装自动程序，该程序可生成具有降低复杂性和足够泛化能力的预测模型。总的来说，所提出的模型可以作为一种替代空间调查工具，成功地用于滑坡易发性制图。
