Landslide is a complex natural hazard that sometimes causes disaster resulting in loss of life, assets and infrastructure, especially in the Himalayas. Recent studies suggest that for effective mitigation and resilience through proper planning and policymaking, it is equally important to justify and select a suitable scientific technique that most appropriately addresses the salient causes of a landslide in any area. The principal objective of this study is to carry out a comparative assessment between two contemporary statistical techniques, i.e., the statistical information value (SIV) and index of entropy (IOE), to find out the effectiveness of the two said methods in landslide susceptibility mapping in Bhanupali-Beri region. During the analysis, the higher-resolution satellite images, i.e., World view-2 image of 2017 and Landsat-8 OLI image of 2018, have been used for delineation of various triggering parameters used for landslide susceptibility. The contemporary GIS technique integrated with the remote sensing applications was distinct in preparing the prominent landslide conditioning factor layers such as slope, slope aspect, thrust and fault proximity, geomorphology, landuse–landcover, stream power index, topographic wetness index, geology, roads proximity, lineament density and past landslide inventory. The final assessment was performed using GIS software through raster re-sampling, and the values derived for each conditioning factors were combined using defined SIV and IOE equations. The study area was categorized into five distinct landslide susceptible zones (very low, low, moderate, high and very high) using the Jenk’s Natural Breaks algorithm. Index of entropy model has given better results compared to SIV. The utmost vital factors triggering landslide (estimated for entropy values) in the area are landuse–landcover with barren land and sparse vegetation followed by TWI, lineament density, geomorphology, and slope.

中文翻译：

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使用统计信息值和熵模型指数的印度喜马偕尔邦 Bhanupali-Beri 地区比较滑坡敏感性评估

滑坡是一种复杂的自然灾害，有时会造成灾难，导致生命、资产和基础设施的损失，尤其是在喜马拉雅山脉。最近的研究表明，为了通过适当的规划和决策进行有效的缓解和恢复，证明和选择最适合解决任何地区滑坡的主要原因的合适的科学技术同样重要。本研究的主要目的是对两种当代统计技术，即统计信息值 (SIV) 和熵指数 (IOE) 进行比较评估，以找出这两种方法在滑坡敏感性绘图中的有效性。在巴努帕里-贝里地区。在分析过程中，更高分辨率的卫星图像，即，2017 年的 World view-2 图像和 2018 年的 Landsat-8 OLI 图像已被用于描绘用于滑坡敏感性的各种触发参数。结合遥感应用的当代 GIS 技术在准备突出的滑坡条件层方面是独特的，例如坡度、坡向、冲断和断层接近度、地貌、土地利用-土地覆盖、河流功率指数、地形湿度指数、地质、道路接近度，线性密度和过去的滑坡清单。最终评估是使用 GIS 软件通过栅格重新采样进行的，并且使用定义的 SIV 和 IOE 方程组合每个条件因子得出的值。研究区分为五个不同的滑坡易感区（极低、低、中、高和非常高）使用 Jenk 的自然中断算法。与 SIV 相比，熵模型的指数给出了更好的结果。引发该地区滑坡的最重要因素（估计熵值）是土地利用-土地覆盖、贫瘠土地和稀疏植被，其次是 TWI、线性密度、地貌和坡度。