Hasan Md Mahmudul, Roy Sujit Kumar, Talha M D, Ferdous Md Tasim, Nasher N M Refat
Department of Geography and Environment, Jagannath University, Dhaka, 1100, Bangladesh.
Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Environ Sci Pollut Res Int. 2024 Sep 20. doi: 10.1007/s11356-024-34949-5.
Landslides pose a severe threat to people, buildings, and infrastructure. The rugged terrain of the Chattogram Hill Tract region in southeastern Bangladesh frequently experiences landslides, particularly during rainy seasons. This study provides a comparative analysis of innovative machine learning (ML) algorithms used for the purpose of landslide susceptibility (LS) mapping for the Khagrachari district of Bangladesh. The dataset for this study comprises 15 landslide conditioning factors and 127 landslide inventory points. The landslide inventory points included 71 landslide and 56 non-landslide points. Then, the data were split randomly into training data (70%) and testing data (30%). Three ML algorithms, namely random forest (RF), boosted regression trees (BRT), and k-nearest neighbor (KNN), were utilized to evaluate the LS zone. The models were validated using the area under the curve (AUC), overall accuracy, precision, and recall. Based on the AUC value, the BRT model demonstrated the highest performance with a value of 0.95, while the AUC values for RF and KNN were 0.91 and 0.86, respectively. Besides, overall accuracy, precision, and recall values (0.82, 0.81, and 0.86) also indicated BRT as the most effective model. The results showed that maximum rainfall and elevation were the most influential factors for both BRT and RF models. This research provides valuable insight into understanding the LS areas in Khagrachari, aiding in informed decision-making regarding landslide-related concerns in the region, and can be applied to the broader scale to develop effective planning and mitigation strategies for comprehensive disaster management and natural hazard response.
山体滑坡对人员、建筑物和基础设施构成严重威胁。孟加拉国东南部吉大港山区地形崎岖，山体滑坡频发，尤其是在雨季。本研究对用于孟加拉国哈格拉乔里地区滑坡易发性（LS）制图的创新机器学习（ML）算法进行了比较分析。本研究的数据集包括15个滑坡条件因子和127个滑坡清单点。滑坡清单点包括71个滑坡点和56个非滑坡点。然后，将数据随机分为训练数据（70%）和测试数据（30%）。利用三种机器学习算法，即随机森林（RF）、增强回归树（BRT）和k近邻（KNN）来评估滑坡易发性区域。使用曲线下面积（AUC）、总体准确率、精确率和召回率对模型进行验证。基于AUC值，BRT模型表现最佳，值为0.95，而RF和KNN的AUC值分别为0.91和0.86。此外，总体准确率、精确率和召回率值（0.82、0.81和0.86）也表明BRT是最有效的模型。结果表明，最大降雨量和海拔高度是BRT和RF模型中最具影响力的因素。本研究为了解哈格拉乔里的滑坡易发性区域提供了有价值的见解，有助于就该地区与滑坡相关的问题做出明智决策，并可在更广泛的范围内应用，以制定有效的规划和缓解策略进行全面的灾害管理和自然灾害应对。
