Department of Watershed Management Engineering, College of Natural Resources and Marine Science, Tarbiat Modares University, Noor, 46414-356, Iran; Department of Forests, Rangelands, and Watershed Management Engineering, Kohgiluyeh & Boyer Ahmad Agricultural and Natural Resources Research and Education Center, AREEO, Yasouj, Iran.
Department of Watershed Management Engineering, College of Natural Resources and Marine Science, Tarbiat Modares University, Noor, 46414-356, Iran.
J Environ Manage. 2021 Oct 1;295:113040. doi: 10.1016/j.jenvman.2021.113040. Epub 2021 Jun 18.
Floods often significantly impact human lives, properties, and activities. Prioritizing areas in a region for mitigation based on flood probability is essential for reducing losses. In this study, two game theory (GT) algorithms - Borda and Condorcet - were used to determine the areas in the Tajan watershed, Iran that were most likely to flood, and two machine learning models - random forest (RF), and artificial neural network (ANN) - were used to model flood probability (the probability of flooding). Twelve independent variables (slope, aspect, elevation, topographic position index (TPI), topographic wetness index (TWI), terrain ruggedness index (TRI), land use, soil, lithology, rainfall, drainage density, and distance to river) and 263 locations of flooding were used to model and prepare flood-probability maps. The RF model was more accurate (AUC = 0.949) than the ANN model (AUC = 0.888). Frequency ratio (FR) was calculated for all factors to determine which had the most influence on flood probability. The values of twelve factors that affect flood probability were estimated for each sub-watershed. Then, game-theory algorithms were used to prioritize sub-watersheds in terms of flood probability. A pairwise comparison matrix revealed that the sub-watersheds most likely to flood. The Condorcet algorithm selected sub-watersheds 1, 2, 4, 5, and 11 and the Borda algorithm selected sub-watersheds 2, 4, 5, 20 and 11. Both models predicted that most of the watershed has very low flood probability and a very small portion has a high probability for flooding. The quantitative analysis and characterization of the watersheds from the perspective of flood hazard can support decision making, planning, and investment in mitigation measures.
洪水经常对人类生命、财产和活动产生重大影响。根据洪水概率对区域进行优先排序，对于减少损失至关重要。在这项研究中，使用了两种博弈论（GT）算法 - 博尔达和孔多塞 - 来确定伊朗塔扬流域最容易发生洪水的区域，并使用两种机器学习模型 - 随机森林（RF）和人工神经网络（ANN） - 来模拟洪水概率（洪水发生的概率）。使用了 12 个独立变量（坡度、方位、海拔、地形位置指数（TPI）、地形湿润指数（TWI）、地形崎岖指数（TRI）、土地利用、土壤、岩性、降雨量、排水密度和到河流的距离）和 263 个洪水位置来模拟和准备洪水概率图。RF 模型比 ANN 模型（AUC = 0.888）更准确（AUC = 0.949）。对所有因素进行频率比（FR）计算，以确定哪些因素对洪水概率影响最大。对每个子流域的 12 个影响洪水概率的因素进行了估计。然后，使用博弈论算法根据洪水概率对子流域进行优先级排序。成对比较矩阵揭示了最有可能发生洪水的子流域。孔多塞算法选择了子流域 1、2、4、5 和 11，博尔达算法选择了子流域 2、4、5、20 和 11。两种模型都预测该流域大部分地区洪水概率非常低，只有一小部分地区洪水概率很高。从洪水灾害的角度对流域进行定量分析和描述，可以为决策制定、规划和投资减灾措施提供支持。
