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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques
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Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

机译：使用四种最新的元启发式技术优化用于滑坡易感性空间预测的自适应神经模糊推理系统

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摘要

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.
机译：包括遗传算法（GA），粒子群优化（PSO），差分进化（DE）和蚁群优化（ACO）在内的四种最新的元启发式算法被应用于自适应神经模糊推理系统（ANFIS） ）用于加兹温省（伊朗）滑坡敏感性的空间预测。为此，由199个已识别的滑坡组成的滑坡清单图被分为以70:30的比例进行训练和测试的滑坡。为了创建空间数据库，在地理信息系统（GIS）中考虑了13个滑坡条件因素。值得注意的是，通过频率比（FR）理论分析了滑坡与上述条件因素之间的空间相互作用。经过优化过程，结果表明基于DE的模型比其他集成更快地达到最佳响应。绘制了滑坡敏感性图，并根据接收工作特征曲线（AUROC），平均绝对误差和均方误差（MSE）精度指标下的计算面积，通过等级系统评估了模型的准确性。根据结果​​，总排名得分（TRS）= 24的GA-ANFIS提供了最准确的预测，其次是PSO-ANFIS（TRS = 17），DE-ANFIS（TRS = 13）和ACO-ANFIS（ TRS = 6）。由于这项研究的出色成果，开发的滑坡敏感性图可用于相关地区的未来规划和决策。

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