Department of Geomorphology, Tarbiat Modares University, Tehran 36581-17994, Iran.
Faculty of Earth Sciences, Kharazmi University, Tehran 14911-15719, Iran.
Sci Total Environ. 2019 Apr 10;660:443-458. doi: 10.1016/j.scitotenv.2019.01.021. Epub 2019 Jan 5.
In north of Iran, flood is one of the most important natural hazards that annually inflict great economic damages on humankind infrastructures and natural ecosystems. The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Therefore, this study tested four methods: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area. These were combined in two methodological frameworks involving statistical and multi-criteria decision making approaches. The efficiency of statistical and multi-criteria methods in FHSM were compared by using area under receiver operating characteristic (AUROC) curve, seed cell area index and frequency ratio. A database containing flood inventory maps and flood-related conditioning factors was established for this watershed. The flood inventory maps produced included 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). Analytical hierarchy process (AHP) indicated that slope, distance to stream and land use/land cover are of key importance in flood occurrence in the study catchment. In validation results, the EBF model had a better prediction rate (0.987) and success rate (0.946) than FR, TOPSIS and VIKOR (prediction rate 0.917, 0.888, and 0.810; success rate 0.939, 0.904, and 0.735, respectively). Based on their frequency ratio and seed cell area index values, all models except VIKOR showed acceptable accuracy of classification.
在伊朗北部，洪水是最重要的自然灾害之一，每年都会给人类基础设施和自然生态系统造成巨大的经济损失。基萨水库流域是伊朗北部的一个重要地区，由于经常发生洪水以及水资源和土壤资源的浪费，以及相关的经济和生态损失，这里已经成为一个多灾多难的地区。然而，目前还没有进行全面系统的研究来确定易发生洪水的地区，而这些地区可能有助于建立管理和保护措施。因此，本研究在该地区测试了四种方法：证据权（EBF）、频率比（FR）、逼近理想解的排序技术（TOPSIS）和 VIKOR 用于洪水灾害易发性制图（FHSM）。这些方法结合了统计学和多准则决策方法两种方法框架。通过使用接收者操作特征（AUROC）曲线、种子单元格面积指数和频率比来比较统计和多准则方法在 FHSM 中的效率。为该流域建立了一个包含洪水清单图和与洪水有关的条件因素的数据库。生成的洪水清单图包括 132 种洪水情况，这些情况被随机分为两组，用于训练（70%）和验证（30%）。层次分析法（AHP）表明，坡度、距溪流的距离和土地利用/土地覆被是该研究流域洪水发生的关键因素。在验证结果中，EBF 模型的预测率（0.987）和成功率（0.946）均高于 FR、TOPSIS 和 VIKOR（预测率分别为 0.917、0.888 和 0.810；成功率分别为 0.939、0.904 和 0.735）。根据它们的频率比和种子单元格面积指数值，除 VIKOR 外，所有模型的分类精度都达到了可接受的水平。
