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首页> 外文OA文献 >Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models
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Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

机译：使用粒子群优化的多层的Multidayer Perceptron的滑坡敏感性预测：与唯一的多层 - Perceptron，BP神经网络和信息价值模型的比较

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摘要

Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.
机译：山体滑坡是一种严重的地质灾害造成当地人民群众生命财产的巨大损失。滑坡敏感性预测（LSP）可以被用来确定在一定区域内滑坡发生的空间概率。实现LSP进行滑坡灾害预防和减少是很重要的。本研究中开发的粒子群优化的多层感知器（PSO-MLP）模型LSP实施克服常规梯度下降算法的缺点，并以确定MLP的最佳的结构参数。石城县在中国江西省作为研究区域。总共369个滑坡，随机选择的非滑坡，和14滑坡相关诱发因素被用于训练和测试本PSO-MLP模型和其他三种对比模型（与梯度下降算法的MLP-唯一模式，背-propagation神经网络（BPNN），和一个信息值（IV）模型）。该结果表明，PSO-MLP模型具有（接收操作的0.822和0.856频率比（FR）精度特性曲线（AUC）下的面积）与MLP-只（0.791和0.829）相比，最准确的预测性能， BP神经网络（0.800和0.840），和IV（0.788和0.824）模型。由此可以得出结论，所提出的PSO-MLP模型地址MLP-唯一模式的弊端以及和执行比传统的人工神经网络（人工神经网络）和统计模型更好。在石城县滑坡发生的空间概率分布规律，深受使用PSO-MLP模型产生的滑坡敏感性图显示。此外，本PSO-MLP模型可以具有与常规的人工神经网络和的统计模型相比较的一些其它字段更高的预测和分类性能。

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