An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/ licenses/by-nc-nd/4.0/). doi: 10.2166/nh.2020.141 s://iwaponline.com/hr/article-pdf/doi/10.2166/nh.2020.141/650359/nh2020141.pdf Naser Dehghanian S. Saeid Mousavi Nadoushani (corresponding author) Department of Water Resources Management, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, I.R. Iran E-mail: sa_mousavi@sbu.ac.ir Bahram Saghafian Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, I.R. Iran Morteza Rayati Damavandi Department of Technical and Engineering, Islamic Azad University, Qaemshahr, Iran

中文翻译：

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评估耦合 ANN-GA 模型以优先考虑未测量流域中的洪水源区

防洪规划的一个重要步骤是确定洪水源区 (FSA)。本研究提出了一种识别 FSA 的方法。已提出单位洪水响应 (UFR) 方法来量化子流域和/或单元尺度的 FSA。在这项研究中，与 Muskingum 流量路由相关的分布式 ModClark 模型用于水文模拟。此外，采用模糊混合聚类方法来识别水文均质区 (HHR)，从而产生涉及通过因子分析 (FA) 选择的径流生成中最有效变量的聚类。将选定的变量连同 50 年的降雨量输入到通过遗传算法 (GA) 优化的人工神经网络 (ANN) 模型中，以预测单元尺度的洪水指数 (FI)。案例研究是两个半干旱的流域，伊朗东北部的 Tangrah 和亚利桑那州的 Walnut Gulch 实验流域。结果表明，在均方误差 (MSE)、平均绝对误差 (MAE) 和相对误差 (RE) 方面，通过 ANN-GA 得出的 FI 预测值与通过 UFR 得出的预测值略有不同。此外，通过 ANN-GA 的优先 FSA 几乎与 UFR 相似。拟议的方法可能适用于在未测量的半干旱流域中根据洪水生成对 HHR 进行优先排序。这是一篇根据知识共享署名许可 (CC BY-NC-ND 4.0) 条款分发的开放获取文章，该许可允许出于非商业目的复制和重新分发，没有衍生品，前提是正确引用原始作品 (http: //creativecommons.org/licenses/by-nc-nd/4.0/)。doi：10.2166/nh.2020.141 s://iwaponline。