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Robustness evaluation of fuzzy expert system and extreme learning machine for geographic information system-based landslide susceptibility zonation: A case study from Indian Himalaya

机译：基于地理信息系统滑坡敏感性区划的模糊专家系统和极限学习机的鲁棒性评估-以印度喜马拉雅山为例

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摘要

In the last few decades, with the development of computers and geographic information system (GIS), a wide range of landslide susceptibility zonation (LSZ) techniques were orchestrated by various researchers around the globe. Among them, the artificial intelligence (AI) have been distinctly regarded as the most effective and suitable approach to part with GIS for LSZ. Though, suitability of AI for LSZ is well addressed in the landslide literature, noises of processing data, choice of causative factors and landslide density of study area are the number of hindrances that cause quandary over preference of ideal AI technique among many. The current study intends to analyse and compare the predictive performance of two entirely different AI techniques, fuzzy expert system (FES), a bivariate statistical technique, and extreme learning machine (ELM), a multivariate statistical technique for GIS based LSZ. The Mussoorie Township, a famous tourist destination in the Indian State of Uttarakhand was taken as the study area. Thematic layers of relevant causative factors and landslide inventory were prepared for the study area through field survey, remote sensing, and GIS. The resultant landslide susceptibility maps (LSM) of the study area, LSM-I of FES and LSM-II of ELM were critically evaluated and compared with the aid of landslide inventory of the study area.
机译：在过去的几十年中，随着计算机和地理信息系统（GIS）的发展，全球各种研究人员精心策划了各种各样的滑坡敏感性分区（LSZ）技术。其中，人工智能（AI）已被明确视为最适合与LSZ进行GIS分离的方法。尽管在滑坡文献中已经很好地解决了AI适用于LSZ的问题，但是处理数据的噪声，病因的选择以及研究区域的滑坡密度是造成众多人对理想AI技术的偏爱的众多障碍。当前的研究旨在分析和比较两种完全不同的AI技术的预测性能，即模糊专家系统（FES），双变量统计技术和极限学习机（ELM），基于GIS的LSZ的多元统计技术。研究区以印度北阿坎德邦州著名的旅游胜地马苏里镇为研究对象。通过实地调查，遥感和GIS为研究区域准备了相关的致病因素和滑坡清单的主题层。研究区的滑坡敏感性图（LSM），FES的LSM-I和ELM的LSM-II进行了严格评估，并借助研究区的滑坡清单进行了比较。

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