Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
J Environ Manage. 2023 Nov 1;345:118838. doi: 10.1016/j.jenvman.2023.118838. Epub 2023 Aug 16.
Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning - an updated version of machine learning techniques - and multi-criteria decision making (MCDM) models can generate high-resolution flood risk maps. In this study, a novel integrated approach has been developed based on multiplicative long short-term memory (mLSTM) deep learning models and an MCDM ensemble model to map flood risk in the Minab-Shamil plain, southern Iran. A flood hazard map generated by the mLSTM model is based on nine critical features selected by GrootCV (distance to the river, vegetation cover, variables extracted from DEM (digital elevation model) and river density) and a flood inventory map (70% and 30% data were randomly selected as training and test datasets, respectively). The values of all criteria used to assess model accuracy performance (except Cohens kappa for train dataset = 86, and for test dataset = 84) achieved values greater than 90, which indicates that the mLSTM model performed very well for the generation of a spatial flood hazard map. According to the spatial flood hazard map produced by mLSTM, the very low, low, moderate, high and very high classes cover 26%, 35.3%, 20.5%, 11.2% and 7% of the total area, respectively. Flood vulnerability maps were produced by the combinative distance-based assessment (CODAS), the evaluation based on distance from average solution (EDAS), and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA), and then validated by Spearman's rank correlation coefficients (SRC). Based on the SRC, the three models CODAS, EDAS, and MOOSRA showed high-ranking correlations with each other, and all three models were then used in the ensemble process. According to the CODAS-EDAS-MOOSRA ensemble model, 21.5%, 34.2%, 23.7%, 13%, and 7.6% of the total area were classified as having a very low to very high flood vulnerability, respectively. Finally, a flood risk map was generated by the combination of flood hazard and vulnerability maps produced by the mLSTM and MCDM ensemble model. According to the flood risk map, 27.4%, 34.3%, 14.8%, 15.7%, and 7.8% of the total area were classified as having a very low, low, moderate, high, and very high flood risk, respectively. Overall, the integration of mLSTM and the MCDM ensemble is a promising tool for generating precise flood risk maps and provides a useful reference for flood risk management.
洪水风险评估是洪水管理和减轻的关键步骤，洪水风险图提供了洪水风险的定量衡量标准。因此，将深度学习——机器学习技术的更新版本——与多准则决策 (MCDM) 模型集成可以生成高分辨率的洪水风险图。在这项研究中，开发了一种基于乘法长短期记忆 (mLSTM) 深度学习模型和 MCDM 集成模型的新方法，以绘制伊朗南部米纳布-沙米尔平原的洪水风险图。mLSTM 模型生成的洪水灾害图基于从 GrootCV 中选择的九个关键特征（到河流的距离、植被覆盖、从数字高程模型 (DEM) 提取的变量和河流密度）和洪水清单图（分别将 70%和 30%的数据随机选择为训练数据集和测试数据集）。用于评估模型准确性性能的所有标准的值（训练数据集的 Cohens kappa 值除外，为 86，测试数据集为 84）都达到了大于 90 的值，这表明 mLSTM 模型非常适合生成空间洪水灾害图。根据 mLSTM 生成的空间洪水灾害图，极低、低、中、高和极高等级分别覆盖总面积的 26%、35.3%、20.5%、11.2%和 7%。洪水脆弱性图是通过基于组合距离的评估 (CODAS)、基于平均解的距离评估 (EDAS) 和基于简单比率分析的多目标优化 (MOOSRA) 生成的，然后通过 Spearman 秩相关系数 (SRC) 进行验证。根据 SRC，CODAS、EDAS 和 MOOSRA 这三个模型之间存在高度相关的关系，并且这三个模型都被用于集成过程。根据 CODAS-EDAS-MOOSRA 集成模型，总区域的 21.5%、34.2%、23.7%、13%和 7.6%分别被归类为具有极低到极高的洪水脆弱性。最后，通过 mLSTM 和 MCDM 集成模型生成的洪水灾害图和脆弱性图的组合生成洪水风险图。根据洪水风险图，总区域的 27.4%、34.3%、14.8%、15.7%和 7.8%分别被归类为具有极低、低、中、高和极高洪水风险。总的来说，mLSTM 和 MCDM 集成是生成精确洪水风险图的有前途的工具，为洪水风险管理提供了有用的参考。
