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Flood susceptibility modelling using advanced ensemble machine learning models
Geoscience Frontiers ( IF 8.9 ) Pub Date : 2020-10-05 , DOI: 10.1016/j.gsf.2020.09.006
Abu Reza Md Towfiqul Islam , Swapan Talukdar , Susanta Mahato , Sonali Kundu , Kutub Uddin Eibek , Quoc Bao Pham , Alban Kuriqi , Nguyen Thi Thuy Linh

Because of the tremendous damage to properties, infrastructures, and human casualties, floods are one of the greatest devastating disasters from nature. Due to the dynamic and complex nature of the flash flood, it is challenging to predict the sites which are vulnerable to flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.



中文翻译:

使用高级集成机器学习模型进行洪水敏感性建模

由于财产,基础设施和人员伤亡受到极大破坏,洪水是自然界最严重的灾难之一。由于山洪暴发的动态和复杂性,要​​预测容易遭受山洪暴发的地点具有挑战性。因此,可以使用先进的机器学习模型来管理洪水灾害,从而更早地识别山洪易受灾地点。在这项研究中,我们应用并评估了两个新的混合集成模型,分别是Dagging和随机子空间(RS)以及人工神经网络(ANN),随机森林(RF)和支持向量机(SVM),它们是另外三个状态最先进的机器学习模型,用于对孟加拉国北部的Teesta河流域的洪水敏感性地图进行建模。这些模型的应用包括在GIS环境中传输的12个洪水影响因子,包括413个当前和以前的洪水点。利用信息增益率,多重共线性诊断测试来确定事故发生与洪水影响因素之间的关联。为了验证和比较这些模型,采用了预测统计评估指标的能力,例如Freidman,Wilcoxon符号秩和t对检验以及接收器工作特性曲线(ROC)。所有模型的ROC曲线下面积(AUC)值均大于0.80。对于洪水敏感性建模,Dagging模型的性能优越,其次是RF,ANN,SVM和RS,然后是几个基准模型。

更新日期:2020-10-05
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