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Integrated machine learning methods with resampling algorithms for flood susceptibility prediction.
Science of the Total Environment ( IF 8.2 ) Pub Date : 2019-12-06 , DOI: 10.1016/j.scitotenv.2019.135983 Esmaeel Dodangeh 1 , Bahram Choubin 2 , Ahmad Najafi Eigdir 2 , Narjes Nabipour 3 , Mehdi Panahi 4 , Shahaboddin Shamshirband 5 , Amir Mosavi 6
Science of the Total Environment ( IF 8.2 ) Pub Date : 2019-12-06 , DOI: 10.1016/j.scitotenv.2019.135983 Esmaeel Dodangeh 1 , Bahram Choubin 2 , Ahmad Najafi Eigdir 2 , Narjes Nabipour 3 , Mehdi Panahi 4 , Shahaboddin Shamshirband 5 , Amir Mosavi 6
Affiliation
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Flood susceptibility projections relying on standalone models, with one-time train-test data splitting for model calibration, yields biased results. This study proposed novel integrative flood susceptibility prediction models based on multi-time resampling approaches, random subsampling (RS) and bootstrapping (BT) algorithms, integrated with machine learning models: generalized additive model (GAM), boosted regression tree (BTR) and multivariate adaptive regression splines (MARS). RS and BT algorithms provided 10 runs of data resampling for learning and validation of the models. Then the mean of 10 runs of predictions is used to produce the flood susceptibility maps (FSM). This methodology was applied to Ardabil Province on coastal margins of the Caspian Sea which faced destructive floods. The area under curve (AUC) of receiver operating characteristic (ROC) and true skill statistic (TSS) and correlation coefficient (COR) were utilized to evaluate the predictive accuracy of the proposed models. Results demonstrated that resampling algorithms improved the performance of Standalone GAM, MARS and BRT models. Results also revealed that Standalone models had better performance with the BT algorithm compared to the RS algorithm. BT-GAM model attained superior performance in terms of statistical measures (AUC = 0.98, TSS = 0.93, COR = 0.91), followed by BT-MARS (AUC = 0.97, TSS = 0.91, COR = 0.91) and BT-BRT model (AUC = 0.95, TSS = 0.79, COR = 0.79). Results demonstrated that the proposed models outperformed the benchmark models such as Standalone GAM, MARS, BRT, multilayer perceptron (MLP) and support vector machine (SVM). Given the admirable performance of the proposed models in a large scale area, the promising results can be expected from these models for other regions.
中文翻译：
具有重采样算法的集成机器学习方法，用于洪水敏感性预测。
洪水敏感性预测依赖于独立模型，并通过一次火车测试数据拆分进行模型校准，从而产生偏差的结果。这项研究提出了基于多次重采样方法，随机子采样（RS）和自举（BT）算法的新型洪水综合敏感性预测模型，并与机器学习模型集成：广义加性模型（GAM），增强回归树（BTR）和多变量自适应回归样条（MARS）。RS和BT算法提供了10次数据重采样运行，以学习和验证模型。然后，使用10次运行预测的平均值来生成洪水敏感性图（FSM）。这种方法被应用于面临破坏性洪水的里海沿岸地区的阿尔达比勒省。利用接收器工作特性（ROC）的曲线下面积（AUC）以及真实技能统计（TSS）和相关系数（COR）来评估所提出模型的预测准确性。结果表明，重采样算法提高了独立GAM，MARS和BRT模型的性能。结果还显示，与RS算法相比，使用BT算法的独立模型具有更好的性能。BT-GAM模型在统计指标（AUC = 0.98，TSS = 0.93，COR = 0.91）方面表现优异，其次是BT-MARS（AUC = 0.97，TSS = 0.91，COR = 0.91）和BT-BRT模型（ AUC = 0.95，TSS = 0.79，COR = 0.79）。结果表明，所提出的模型优于诸如独立GAM，MARS，BRT，多层感知器（MLP）和支持向量机（SVM）等基准模型。
更新日期：2019-12-07
中文翻译：
具有重采样算法的集成机器学习方法，用于洪水敏感性预测。
洪水敏感性预测依赖于独立模型，并通过一次火车测试数据拆分进行模型校准，从而产生偏差的结果。这项研究提出了基于多次重采样方法，随机子采样（RS）和自举（BT）算法的新型洪水综合敏感性预测模型，并与机器学习模型集成：广义加性模型（GAM），增强回归树（BTR）和多变量自适应回归样条（MARS）。RS和BT算法提供了10次数据重采样运行，以学习和验证模型。然后，使用10次运行预测的平均值来生成洪水敏感性图（FSM）。这种方法被应用于面临破坏性洪水的里海沿岸地区的阿尔达比勒省。利用接收器工作特性（ROC）的曲线下面积（AUC）以及真实技能统计（TSS）和相关系数（COR）来评估所提出模型的预测准确性。结果表明，重采样算法提高了独立GAM，MARS和BRT模型的性能。结果还显示，与RS算法相比，使用BT算法的独立模型具有更好的性能。BT-GAM模型在统计指标（AUC = 0.98，TSS = 0.93，COR = 0.91）方面表现优异，其次是BT-MARS（AUC = 0.97，TSS = 0.91，COR = 0.91）和BT-BRT模型（ AUC = 0.95，TSS = 0.79，COR = 0.79）。结果表明，所提出的模型优于诸如独立GAM，MARS，BRT，多层感知器（MLP）和支持向量机（SVM）等基准模型。
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