Abstract

Idukki district faced adverse mishappenings during the 2018 Kerala landslides due to incessant torrential rainfall. This study emphasizes developing an efficient and accurate ANN model to integrate the data, process and generate landslide susceptibility maps. Fifteen conditioning factors that influence landslides' occurrence opted in the study constitutes 49 input neurons to the ANN model (L₄₉). Seven inputs with high robustness were identified using the sensitivity analysis approach and were adopted to generate a new ANN model (L₇). Both ANN models were processed to obtain an optimal output with lesser cross-entropy error. The landslide susceptibility maps derived from these ANN models show similar trends with the region's observed landslide locations. The ANN models were validated using ROC, and it provided a very good fit with AUC values of 0.91 and 0.83 as prediction rate for ANN models L₄₉ and L_7, respectively.

摘要

由于持续的暴雨，Idukki地区在2018年的喀拉拉邦滑坡期间面临恶劣的环境。这项研究强调开发一种有效且准确的ANN模型，以整合数据，处理并生成滑坡敏感性图。在研究中选择的十五个影响滑坡发生的条件因素构成了ANN模型的49个输入神经元（L ₄₉）。使用灵敏度分析方法确定了具有高鲁棒性的七个输入，并采用这些输入来生成新的ANN模型（L ₇）。两种神经网络模型都经过处理以获得具有较小交叉熵误差的最优输出。从这些人工神经网络模型得出的滑坡敏感性图显示出与该地区观测到的滑坡位置相似的趋势。使用ROC验证了ANN模型，它以0.91和0.83的AUC值分别很好地拟合了ANN模型L ₄₉和L _7，从而提供了很好的拟合度。