Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam.
Center for Agricultural Research and Ecological Studies (CARES), Vietnam National University of Agriculture, Trau Quy, Gia Lam, Hanoi, 100000, Viet Nam.
J Environ Manage. 2021 Feb 15;280:111858. doi: 10.1016/j.jenvman.2020.111858. Epub 2020 Dec 23.
Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains challenges because of its complexity and rapid onset of precipitation. Thus, this study aims to propose a state-of-the-art data mining approach based on a hybrid equilibrium optimized SysFor, namely, the HE-SysFor model, for spatial prediction of flash floods. A tropical storm region located in the Northwest areas of Vietnam is selected as a case study. For this purpose, 1866 flash-flooded locations and ten indicators were used. The results show that the proposed HE-SysFor model yielded the highest predictive performance (total accuracy = 93.8%, Kappa index = 0.875, F1-score = 0.939, and AUC = 0.975) and produced the better performance than those of the C4.5 decision tree (C4.5), the radial basis function-based support vector machine (SVM-RBF), the logistic regression (LReg), and deep learning neural network (DeepLNN) models in both the training and the testing phases. Among the ten indicators, elevation, slope, and land cover are the most important. It is concluded that the proposed model provides an alternative tool and may help for effectively monitoring flash floods in tropical areas and robust policies for decision making in mitigating the flash flood impacts.
山洪是最危险的水文和自然现象之一，由于其突发性强和个体死亡比例高，被认为是各种自然灾害中排名最高的灾害之一。由于其复杂性和降水的快速发生，山洪事件的概率测绘仍然具有挑战性。因此，本研究旨在提出一种基于混合平衡优化 SysFor 的最先进的数据挖掘方法，即 HE-SysFor 模型，用于山洪的空间预测。选择越南北部地区的一个热带风暴区域作为案例研究。为此，使用了 1866 个山洪暴发地点和十个指标。结果表明，所提出的 HE-SysFor 模型具有最高的预测性能（总准确率为 93.8%，kappa 指数为 0.875，F1 分数为 0.939，AUC 为 0.975），并且在训练和测试阶段都优于 C4.5 决策树（C4.5）、基于径向基函数的支持向量机（SVM-RBF）、逻辑回归（LReg）和深度学习神经网络（DeepLNN）模型的性能。在这十个指标中，海拔、坡度和土地覆盖是最重要的。结论是，所提出的模型提供了一种替代工具，可以帮助有效地监测热带地区的山洪，并为减轻山洪影响的决策制定提供强有力的政策。
