Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Department of Watershed Sciences and Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
Sci Total Environ. 2018 Feb 15;615:438-451. doi: 10.1016/j.scitotenv.2017.09.262. Epub 2017 Oct 5.
Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses.
洪水是最具破坏性的自然灾害之一，每年都会造成巨大的财务和生命损失。因此，为了减少其危害，制作洪水管理易感性图是必要的。本研究的目的是利用自适应神经模糊推理系统（ANFIS）与不同的元启发式算法（如蚁群优化（ACO）、遗传算法（GA）和粒子群优化（PSO））相结合，绘制法尔斯省 Jahrom 镇的洪水灾害图，并比较它们的准确性。总共确定了 53 个洪水发生区域，其中 35 个区域被随机选择用于建模洪水易感性，其余 16 个区域用于验证模型。学习向量量化（LVQ）作为一种有监督的神经网络方法，用于估计因素的重要性。选择了 9 个洪水影响因素，包括坡度、平面曲率、海拔、地形湿度指数（TWI）、水流功率指数（SPI）、距河流的距离、土地利用/土地覆盖、降雨量和岩性，并在 ArcGIS 中准备了相应的地图。频率比（FR）模型用于为特定控制因素中的每个类别分配权重，然后将权重转移到 MATLAB 软件中进行进一步分析，并与元启发式模型相结合。结果表明，ANFIS-PSO 模型在生成高度集中的洪水易感性图方面最为实用，其空间分布与高度易感性类别相关较小。卡方检验结果也证明了这一点，其中 ANFIS-PSO 模型在研究区域内洪水易感性类别之间具有最高的空间差异。ROC 曲线获得的曲线下面积（AUC）分别表示 FR、ANFIS-ACO、ANFIS-GA 和 ANFIS-PSO 模型的准确率为 91.4%、91.8%、92.6%和 94.5%。因此，引入了 ANFIS-PSO 模型作为研究区域的主要模型。此外，LVQ 结果表明，坡度、降雨量和海拔是最有效的因素。就主要模型而言，总面积的 44.74%被认为极易受到洪水影响。本研究的结果可作为更好的土地利用规划的平台，以管理易受洪水影响的区域并减少预期损失。
