Landslides are geological hazards that can have severe impacts, threatening both the people and the local environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting landslides and mitigating landslide-associated damage in areas prone to these events. This study aims to investigate the combination of using an adaptive network-based fuzzy inference system (ANFIS) with metaheuristic optimization algorithms: gray wolf optimizer (GWO), particle swarm optimization algorithm (PSO), and the imperialist competitive algorithm (ICA) in mapping landslide potential. The study area was Pyeongchang-gun, South Korea, for which an accurate landslide inventory dataset is available. A landslide inventory map was organized, and the data were separated randomly into training data (70%) and validation data (30%). In addition, 16 landslide-related factors consisting of geo-environmental and topo-hydrological factors were considered as predictive variables. This landslide susceptibility model was be evaluated based on the value of the area under the receiver operating characteristic (ROC) curve (AUC) to measure its accuracy. Based on the maps, the validation results showed that the optimized models of ANFIS-ICA, ANFIS-PSO, and ANFIS-GWO had AUC accuracies of 0.927, 0.947, and 0.968, respectively. The result from the hybrid algorithms model of ANFIS with metaheuristic algorithms outperformed the standalone ANFIS model in terms of accuracy in predicting landslide potential. Therefore, the ML algorithm and optimization algorithm models proposed in this study are more suitable for landslide susceptibility mapping in the study area.

滑坡是一种地质灾害，可以产生严重的影响，威胁到高地或山坡的人民和当地环境。滑坡敏感性绘图是预测滑坡和减轻易发生这些事件的地区的滑坡相关损害的重要工具。本研究旨在研究在映射中使用基于自适应网络的模糊推理系统 (ANFIS) 与元启发式优化算法的组合：灰狼优化器 (GWO)、粒子群优化算法 (PSO) 和帝国主义竞争算法 (ICA)滑坡潜力。研究区域是韩国平昌郡，这里有准确的滑坡清单数据集。组织了滑坡清单图，并将数据随机分为训练数据（70%）和验证数据（30%）。此外，包括地质环境和地形水文因素在内的16个滑坡相关因素被认为是预测变量。该滑坡敏感性模型是根据接受者操作特征（ROC）曲线（AUC）下面积的值来评估的，以测量其准确性。基于图，验证结果表明，ANFIS-ICA、ANFIS-PSO 和 ANFIS-GWO 优化模型的 AUC 精度分别为 0.927、0.947 和 0.968。在预测滑坡潜力的准确性方面，ANFIS 与元启发式算法的混合算法模型的结果优于独立的 ANFIS 模型。因此，本研究提出的 ML 算法和优化算法模型更适用于研究区滑坡敏感性填图。包括地质环境和地形水文因素在内的16个滑坡相关因素被认为是预测变量。该滑坡敏感性模型是根据接受者操作特征（ROC）曲线（AUC）下面积的值来评估的，以测量其准确性。基于图，验证结果表明，ANFIS-ICA、ANFIS-PSO 和 ANFIS-GWO 优化模型的 AUC 精度分别为 0.927、0.947 和 0.968。在预测滑坡潜力的准确性方面，ANFIS 与元启发式算法的混合算法模型的结果优于独立的 ANFIS 模型。因此，本研究提出的 ML 算法和优化算法模型更适用于研究区滑坡敏感性填图。包括地质环境和地形水文因素在内的16个滑坡相关因素被认为是预测变量。该滑坡敏感性模型是根据接受者操作特征（ROC）曲线（AUC）下面积的值来评估的，以测量其准确性。基于图，验证结果表明，ANFIS-ICA、ANFIS-PSO 和 ANFIS-GWO 优化模型的 AUC 精度分别为 0.927、0.947 和 0.968。在预测滑坡潜力的准确性方面，ANFIS 与元启发式算法的混合算法模型的结果优于独立的 ANFIS 模型。因此，本研究提出的 ML 算法和优化算法模型更适用于研究区滑坡敏感性填图。