在这项研究中,预测滑坡敏感性图(LSM),我们通过利用回溯搜索算法(BSA)和布谷鸟优化算法(COA)研究和优化了人工神经网络(ANN)。多项研究表明,基于人工神经网络的技术可用于计算LSM。尽管如此,人工神经网络计算模型有很大的问题,比如缓慢的系统学习和陷入局部最小值。优化策略可以改善ANN性能结果。BSA和COA模型在ANN训练中的现有用途尚未用于绘制滑坡图,也没有检查建立网络的最佳方法或影响此问题的其他因素。因此,本研究的重点是使用基于BSA和COA的混合ANN算法(BSA-MLP和COA)预测危险制图的滑坡敏感性。从库尔德斯坦省的一个地区提供了大量数据集,伊朗西部,为算法提供训练和测试数据集。所有的BSA和COA算法参数和权重,例如,进行了微调,以制作最准确的滑坡风险图。输入数据集由高程,斜角,坡度方向,NDVI,容错,轮廓曲率,平面曲率,距离河流,降雨,远离道路,SPI,STI,TRI,TWI,土地利用,和地质;输出为滑坡敏感性值。在测试阶段,使用上述技术后,BSA-MLP的AUC从0.701显著上升至0.864,COA-MLP的AUC从0.738显著上升至0.822。我们已经使用曲线下面积(AUC)来评估概率模型的工作情况。此外,对于COA-MLP组合,BSA-MLP可用数据库的计算AUC和实际AUC分别为0.864,0.857,0.833,0.778,0.777,0.769,0.763,0.758,0.727,0.701和0.822,0.808,0.807,0.805,0.804,0.777和0.769.集成模型可以为这一研究领域产生有益的结果。结果表明,BSA-ANN模型在优化人工神经网络模型的结构和计算参数方面优于COA-ANN。收集的滑坡敏感性图对于弄清楚研究区域中滑坡的危险程度具有重要意义。
In this research, to predict landslide susceptibility mapping (LSM), we have studied and optimized an artificial neural network (ANN) by utilizing the backtracking search algorithm (BSA) as well as the Cuckoo optimization algorithm (COA). Multiple research studies have shown that ANN-based techniques can be used to figure out the LSM. Still, ANN computing models have big problems, like slow system learning and getting stuck in their local minimums. Optimization strategies may improve ANN performance results. Existing uses of the BSA and COA models in ANN training have not been used to map landslides, nor have the best ways to set up networks or other factors that affect this problem been examined. Consequently, the present research focuses on predicting landslide susceptibility for hazardous mapping using hybrid BSA and COA-based ANN algorithms (BSA-MLP and COA). A large data set was provided from an area in the province of Kurdistan, west of Iran, to provide training and testing datasets for the algorithms. All of the BSA and COA algorithms\' parameters and weights, for instance, were fine-tuned to make the utmost accurate maps of landslide risk. The input dataset consists of elevation, slope angle, slope orientation, NDVI, fault tolerance, profile curvature, plan curvature, distance to the river, rainfall, far from the road, SPI, STI, TRI, TWI, land use, and geology; the output is landslide susceptibility value. In the testing phase, the AUC rose significantly from 0.701 to 0.864 for BSA-MLP and 0.738 to 0.822 for COA-MLP after using the abovementioned techniques. We have used the area under the curve (AUC) to evaluate how well the probabilistic models worked. In addition, the computed AUCs for the BSA-MLP available databases and the actual AUCs were 0.864, 0.857, 0.833, 0.778, 0.777, 0.769, 0.763, 0.758, 0.727, and 0.701 and 0.822, 0.808, 0.807, 0.805, 0.804, 0.777, and 0.769 for the COA-MLP combination. The integrated models can produce beneficial results for this area of research. The results suggest that the BSA-ANN model is better than the COA-ANN in optimizing an artificial neural network model\'s structure and computational parameters. The collected landslide susceptibility maps are significant for figuring out how dangerous landslides are in the studied area.