Landslide susceptibility zonation (LSZ) has generally been regarded as the appropriate stride to begin scientific studies in mountainous terrains to alleviate the socio-economic consequences of landslides. Application of machine learning (ML) with geographic information system (GIS) is a promising fusion of technologies, for spatial prediction of landslide susceptibility with high precision, and has been applied widely in the past. However, the literatures of ML and GIS-based LSZ gives a fuzzy conclusion upon the righteous choice of ML technique among many state-of-the-art techniques, and do not present a probe on the aptitude of ML models for township level LSZ attempts. This research investigates such concern with a case study to figure out a robust technique, which can be a benchmark approach in future case studies and various comparisons strives across the sundry genre of ML. For that, the present attempt has been anchored to four different supervised ML algorithms including artificial neural network (ANN), extreme learning machine (ELM) of neural network (NN) genre, classical ML algorithm of support vector machine (SVM) and extreme learning adaptive neuro fuzzy inference system (ELANFIS) of neuro-fuzzy system genre. The Mussoorie Township, a famed hill station in the Indian State of Uttarakhand was chosen as the area for case study. A total of 13 landslide susceptibility maps (LSM) were produced. Spatial performance of these maps was compared and statistically validated with the help of landslide inventory of the study area. Amongst the LSMs, the LSM-ELANFIS-VII of ELANFIS model with 11 number of membership functions (MF) was found to be in better agreement with all the validation measures performed. In addition to the satisfactory performance on validation, the LSMs produced through ELANFIS display a unique trace of geomorphological features on it along with pragmatic scattering of landslide susceptibility classes - an omen that exhorts graduation of GIS-based LSZ to ensemble neuro-fuzzy ML models.

滑坡易感性分区（LSZ）通常被视为在山区进行科学研究以减轻滑坡的社会经济后果的适当步伐。机器学习（ML）与地理信息系统（GIS）的应用是一种有前途的技术融合，可以高精度地对滑坡敏感性进行空间预测，并且在过去已得到广泛应用。但是，基于ML和GIS的LSZ的文献对许多最新技术中ML技术的正确选择给出了模糊的结论，而没有对ML模型在乡镇级LSZ尝试中的适用性进行探讨。 。这项研究通过案例研究调查了这种担忧，以找出可靠的技术，它可以作为未来案例研究的基准方法，并且在ML的各种类型中都进行了各种比较。为此，目前的尝试已锚定到四种不同的监督ML算法，包括人工神经网络（ANN），神经网络（NN）类型的极限学习机（ELM），支持向量机（SVM）的经典ML算法和极限学习模糊系统类型的自适应神经模糊推理系统（ELANFIS）。印度北阿坎德邦州著名的山站Mussoorie Township被选为案例研究区域。总共制作了13张滑坡敏感性图（LSM）。在研究区域的滑坡清单的帮助下，比较了这些地图的空间性能并进行了统计验证。在LSM中，结果发现，具有11个隶属度函数（MF）的ELANFIS模型的LSM-ELANFIS-VII与执行的所有验证措施之间的一致性更高。除了令人满意的验证性能外，通过ELANFIS生产的LSM在其上还显示出独特的地貌特征轨迹以及滑坡易感性类别的务实散射-预示着将基于GIS的LSZ分级升级为集成的神经模糊ML模型的预兆。