Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran; Near East University, Faculty of Civil and Environmental Engineering, Near East Boulevard, 99138, Nicosia, North Cyprus, via Mersin 10, Turkey.
J Environ Manage. 2021 Aug 1;291:112731. doi: 10.1016/j.jenvman.2021.112731. Epub 2021 May 4.
Flooding is a destructive natural phenomenon that causes many casualties and property losses in different parts of the world every year. Efficient flood susceptibility mapping (FSM) can reduce the risk of this hazard, and has become the main approach in flood risk management. In this study, we evaluated the prediction ability of artificial neural network (ANN) algorithms for hard and soft supervised machine learning classification in FSM by using three ANN algorithms (multi-layer perceptron (MLP), fuzzy adaptive resonance theory (FART), self-organizing map (SOM)) with different activation functions (sigmoidal (-S), linear (-L), commitment (-C), typicality (-T)). We used integration of these models for predicting the spatial expansion probability of flood events in the Ajichay river basin, northwest Iran. Inputs to the ANN were spatial data on 10 flood influencing factors (elevation, slope, aspect, curvature, stream power index, topographic wetness index, lithology, land use, rainfall, and distance to the river). The FSMs obtained as model outputs were trained and tested using flood inventory datasets earned based on previous records of flood damage in the region for the Ajichay river basin. Sensitivity analysis using one factor-at-a-time (OFAT) and all factors-at-a-time (AFAT) demonstrated that all influencing factors had a positive impact on modeling to generate FSM, with altitude having the greatest impact and curvature the least. Model validation was carried out using total operating characteristic (TOC) with an area under the curve (AUC). The highest success rate was found for MLP-S (92.1%) and the lowest for FART-T (75.8%). The projection rate in the validation of FSMs produced by MLP-S, MLP-L, FART-C, FART-T, SOM-C, and SOM-T was found to be 90.1%, 89.6%, 71.7%, 70.8%, 83.8%, and 81.1%, respectively. While integration of hard and soft supervised machine learning classification with activation functions of MLP-S and MLP-L showed a strong flood prediction capability for proper planning and management of flood hazards, MLP-S is a promising method for predicting the spatial expansion probability of flood events.
洪水是一种具有破坏性的自然现象，每年在世界不同地区都会造成许多人员伤亡和财产损失。有效的洪水易感性图（FSM）可以降低这种灾害的风险，已成为洪水风险管理的主要方法。在本研究中，我们评估了三种具有不同激活函数（Sigmoidal(-S)、线性(-L)、承诺(-C)、典型性(-T)）的人工神经网络（ANN）算法（多层感知器（MLP）、模糊自适应共振理论（FART）、自组织映射（SOM））在洪水易感性图中的硬监督和软监督机器学习分类的预测能力。我们使用这些模型的集成来预测伊朗西北部阿吉恰伊河流域洪水事件的空间扩展概率。ANN 的输入是 10 个洪水影响因素（海拔、坡度、方位、曲率、水流功率指数、地形湿润指数、岩性、土地利用、降雨量和到河流的距离）的空间数据。作为模型输出的洪水易感性图是在阿吉恰伊河流域基于该地区以前的洪水破坏记录获得的洪水清单数据集上进行训练和测试的。使用逐个因素（OFAT）和所有因素同时（AFAT）的敏感性分析表明，所有影响因素对生成洪水易感性图的建模都有积极影响，其中海拔的影响最大，曲率的影响最小。使用总操作特征（TOC）和曲线下面积（AUC）进行模型验证。MLP-S 的成功率最高（92.1%），FART-T 的最低（75.8%）。在验证 MLP-S、MLP-L、FART-C、FART-T、SOM-C 和 SOM-T 生成的洪水易感性图时，投影率分别为 90.1%、89.6%、71.7%、70.8%、83.8%和 81.1%。虽然硬监督和软监督机器学习分类与 MLP-S 和 MLP-L 的激活函数的集成显示出对洪水灾害进行适当规划和管理的强大洪水预测能力，但 MLP-S 是预测洪水事件空间扩展概率的一种很有前途的方法。
