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A novel per pixel and object-based ensemble approach for flood susceptibility mapping
Geomatics, Natural Hazards and Risk ( IF 4.5 ) Pub Date : 2020-01-01 , DOI: 10.1080/19475705.2020.1833990
Thimmaiah Gudiyangada Nachappa 1 , Sansar Raj Meena 1
Affiliation  

Abstract Conducting flood susceptibility assessments is critical for the identification of flood hazard zones and the mitigation of the detrimental impacts of floods in the future through improved flood management measures. The significance of this study is that we create ensemble methods using the per-pixel approaches of frequency ratio (FR), analytical hierarchical process (AHP), and evidence belief function (EBF) used for weightings with the object-based ‘geons’ approach used for aggregation to create a flood susceptibility map for the East Rapti Basin in Nepal. We selected eight flood conditioning factors considered to be relevant in the study area. The flood inventory data for the East Rapti basin was derived from past flood inventory datasets held in the regional database system by the International Centre for Integrated Mountain Development (ICIMOD). The flood inventory was classified into training and validation datasets based on the widely used split ratio of 70/30. The Receiver Operating Characteristic (ROC) was used to determine the accuracy of the flood susceptibility maps. The AUC results indicated that the combined per-pixel and object-based geon approaches yielded better results than the per-pixel approaches alone. Our results showed that the object-based geon approach creates meaningful regional units that are beneficial for future planning.

中文翻译:

一种新的每像素和基于对象的集成方法,用于洪水敏感性映射

摘要 进行洪水敏感性评估对于识别洪水危险区和通过改进洪水管理措施减轻未来洪水的不利影响至关重要。本研究的意义在于,我们使用频率比 (FR)、分析分层过程 (AHP) 和证据置信函数 (EBF) 的每像素方法创建集成方法,这些方法用于基于对象的“geons”方法进行加权用于聚合以创建尼泊尔东拉普蒂盆地的洪水敏感性图。我们选择了被认为与研究区域相关的八个洪水调节因子。East Rapti 盆地的洪水清单数据来自国际山区综合发展中心 (ICIMOD) 区域数据库系统中保存的过去洪水清单数据集。根据广泛使用的 70/30 分割比,洪水清单被分为训练数据集和验证数据集。接收器操作特性 (ROC) 用于确定洪水敏感性图的准确性。AUC 结果表明,结合每像素和基于对象的 geon 方法比单独使用每像素方法产生更好的结果。我们的结果表明,基于对象的 geon 方法创建了有意义的区域单元,有利于未来的规划。接收器操作特性 (ROC) 用于确定洪水敏感性图的准确性。AUC 结果表明,结合每像素和基于对象的 geon 方法比单独使用每像素方法产生更好的结果。我们的结果表明,基于对象的 geon 方法创建了有意义的区域单元,有利于未来的规划。接收器操作特性 (ROC) 用于确定洪水敏感性图的准确性。AUC 结果表明,结合每像素和基于对象的 geon 方法比单独使用每像素方法产生更好的结果。我们的结果表明,基于对象的 geon 方法创建了有意义的区域单元,有利于未来的规划。
更新日期:2020-01-01
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