Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
School of Engineering and Technology, Duy Tan University, Da Nang, Vietnam.
Environ Sci Pollut Res Int. 2023 Jul;30(34):82964-82989. doi: 10.1007/s11356-023-28133-4. Epub 2023 Jun 19.
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.
在这项研究中，为了预测滑坡易发性制图（LSM），我们研究并优化了一种人工神经网络（ANN），利用回溯搜索算法（BSA）和布谷鸟优化算法（COA）。多项研究表明，基于 ANN 的技术可用于确定 LSM。但是，ANN 计算模型存在一些大问题，例如系统学习缓慢和陷入局部最小值。优化策略可以提高 ANN 性能结果。BSA 和 COA 模型在 ANN 训练中的现有应用尚未用于滑坡制图，也没有研究最佳的网络设置方式或影响此问题的其他因素。因此，本研究侧重于使用基于混合 BSA 和 COA 的 ANN 算法（BSA-MLP 和 COA）预测危险制图中的滑坡易发性。从伊朗西部库尔德斯坦省的一个地区提供了一个大数据集，为算法提供了培训和测试数据集。例如，调整了所有 BSA 和 COA 算法的参数和权重，以使滑坡风险的最准确地图。输入数据集由海拔、坡度角、坡度方向、NDVI、容错、剖面曲率、平面曲率、与河流的距离、降雨量、远离道路、SPI、STI、TRI、TWI、土地利用和地质组成；输出是滑坡易发性值。在测试阶段，使用上述技术后，BSA-MLP 的 AUC 从 0.701 显著上升到 0.864，COA-MLP 的 AUC 从 0.738 上升到 0.822。我们使用曲线下面积（AUC）来评估概率模型的工作效果。此外，BSA-MLP 可用数据库计算的 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 和 0.701，COA-MLP 组合的 AUC 分别为 0.822、0.808、0.807、0.805、0.804、0.777 和 0.769。综合模型可为该研究领域提供有益的结果。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。收集的滑坡易发性图对于确定研究区域内滑坡的危险性具有重要意义。
