School of Civil and Environmental Engineering, Indian Institute of Technology-Mandi, Mandi, Himachal Pradesh, India.
Department of Geology, University of Delhi, Delhi, India.
Environ Sci Pollut Res Int. 2024 Sep;31(41):53767-53784. doi: 10.1007/s11356-023-28966-z. Epub 2023 Aug 11.
The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation. A total of 322 landslide points were randomly divided into training (70%) and testing (30%) datasets. Nine causative factors were utilized for landslide susceptibility mapping (LSM). The importance of which was obtained using the information gain (IG) method. FR, IoV, WoE, and WLC were used to prepare the LSM using the training datasets and nine causative factors. Moreover, the accuracy and consistency were evaluated using AUC-ROC, precision, recall, overall accuracy (OA), balanced accuracy (BA), and F-score. The validation results showed that all methods performed well with the highest AUC and precision values of 0.913 and 0.95, respectively, for the IoV method, while the WLC method had the highest OA, BA, and F-score values of 0.808, 0.81, and 0.812, respectively. Finally, the results from LSM were used to evaluate the state of landslide susceptibility along the Imphal-Jiribam railway corridor. The results showed that 34% of the areas had high and very high susceptibility, while 40% were under less and significantly less susceptibility. The Tupul landslide area lay in medium susceptibility where the disastrous landslide occurred on 30 June 2022. Susceptibility values around the Noney and Khongsag railway station ranged from high to very high susceptibility. Thus, the study manifests the need for LSM preparation in rapidly constructing areas, which in turn will help the policymakers and planners for adopting strategies to minimize losses caused due to landslides.
印度东北部地区的基础设施项目和山体滑坡都有所增加。本研究旨在制作印度曼尼普尔邦坦萌龙和森帕蒂地区的滑坡易发性图，并评估因帕尔-哲雷姆铁路沿线的滑坡易发性状况。在模型准备过程中，使用了频率比（FR）、信息量（IoV）、证据权重（WoE）和加权线性组合（WLC）等高效统计方法。总共随机划分了 322 个滑坡点，分为训练（70%）和测试（30%）数据集。利用九个诱发因素进行滑坡易发性制图（LSM）。利用信息增益（IG）法获得了这些因素的重要性。使用 FR、IoV、WoE 和 WLC 利用训练数据集和九个诱发因素准备 LSM。此外，还使用 AUC-ROC、精度、召回率、总体精度（OA）、平衡精度（BA）和 F 值评估准确性和一致性。验证结果表明，所有方法的表现都很好，IoV 方法的 AUC 和精度最高，分别为 0.913 和 0.95，而 WLC 方法的 OA、BA 和 F 值最高，分别为 0.808、0.81 和 0.812。最后，利用 LSM 评估因帕尔-哲雷姆铁路沿线的滑坡易发性状况。结果表明，34%的地区具有高和极高的易发性，而 40%的地区处于低和显著低的易发性。图普尔滑坡区处于中等易发性，2022 年 6 月 30 日发生了灾难性滑坡。农尼和洪萨火车站周围的滑坡易发性值从高到极高。因此，该研究表明在快速建设地区需要进行 LSM 准备，这反过来将有助于决策者和规划者采取策略，以最大限度地减少滑坡造成的损失。
