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基于GIS的数据驱动的双变量统计模型用于预测印度上蒂斯塔盆地的山体滑坡易感性

Jayanta Das¹, Pritam Saha², Rajib Mitra³

¹Department of Geography, Rampurhat College, PO- Rampurhat, Dist- Birbhum, 731224, India.

|May 26, 2023

概括

这项研究比较了五个基于GIS的模型,用于在达吉林-锡金喜马拉雅山区绘制山体滑坡易感性地图. 的指数 (IOE) 模型显示了最高的准确性,确定了用于规划的关键危险区域.

科学领域:

地质科学地质科学
环境科学环境科学
遥感遥感遥感遥感

背景情况:

滑坡预测对于山区的可持续发展至关重要.
达吉林-锡金喜马拉雅山脉非常容易发生山体滑坡.
有效的山体滑坡易感性地图 (LSMs) 是需要的,以减轻危险和土地利用规划.

研究的目的:

为了比较五个基于GIS的两变量统计模型的性能,用于绘制山体滑坡易感性的地图.
确定最准确的模型,用于预测上蒂斯塔盆地易发生山体滑坡的地区.
为灾害管理和土地利用规划提供可靠的LSM.

主要方法:

使用地理信息系统 (GIS) 和遥感技术.
编制了477个山体滑坡地点的山体滑坡库存地图,70%用于培训,30%用于验证.
采用了五种数据驱动的双变量统计模型:频率比 (FR),率指数 (IOE),统计指数 (SI),修改信息价值模型 (MIV) 和证据信念函数 (EBF).
整合了十四个滑坡触发参数:高度,斜率,侧面,曲率,粗度,溪流功率指数,TWI,距离溪流,距离道路,NDVI,LULC,降雨量,修改的Fournier指数和石质学.

主要成果:

度指数 (IOE) 模型实现了最高的训练精度 (95.80%),其次是SI (92.60%),MIV (92.20%),FR (91.50%),EBF (89.90%).
模型确定了高和非常高的山体滑坡易发生地区的不同百分比:FR (12.00%),MIV (21.46%),IOE (28.53%),SI (31.42%) 和EBF (14.17%).
危险区主要位于提斯塔河和主要道路沿线,与山体滑坡分布一致.

结论:

建议使用IOE模型,因为它在研究区域的山体滑坡易感性测绘中具有卓越的准确性.
开发的LSM是准确的,适合减缓山体滑坡和长期土地利用规划.
该方法可应用于其他喜马拉雅地区进行山体滑坡危险评估和管理.

关键词:

证据是信念的功能,是证据的功能.频率比率是一个频率比率.的指数的指数土地滑坡易感性绘制地图这就是ROC-AUC.锡金喜马拉雅地区地区.

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