Flood susceptibility prediction using tree-based machine learning models in the GBA
Flood susceptibility prediction using tree-based machine learning models in the GBA
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DOI：
10.1016/j.scs.2023.104744
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发表时间：
2023-10
影响因子：
11.7
通讯作者：
Haifang Lyu;Z. Yin
中科院分区：
文献类型：
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作者：
Haifang Lyu;Z. Yin
粤港澳大湾区（GBA）频繁遭受洪水和台风的侵袭。本研究利用基于树的机器学习（ML）和地理信息系统技术开发了大湾区洪水易感性评估框架。基于洪水清单，采用随机森林、梯度增强决策树、极端梯度增强和考虑地形、暴露和脆弱性的分类增强等基于树的模型对ML模型进行训练和测试，并将训练后的模型用于洪水易感性预测。所有基于树的ML模型都取得了良好的性能，准确率值大于0.79。分类增强模型对洪水敏感性的预测效果较好。洪水易感度图显示，大湾区洪水易感度高的地区占比超过16%，近70%的历史洪水发生在高易感度地区。Shapley加性解释值总结的模型解释表明，高程、人口密度、台风强度等影响因子对洪涝易感性影响较大。所得空间洪水易感度为大湾区洪水减灾提供了建议。
The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) frequently suffered from floods accompanied with typhoons. This study developed a framework for evaluating flood susceptibility in the GBA using tree-based machine learning (ML) and geographical information system techniques. Based on the flood inventory, tree-based models, namely random forest, gradient boost decision tree, extreme gradient boosting, and categorical boosting considering topography, exposure, and vulnerability as influential factors, were used to train and test ML models, and the trained models were then used to predict flood susceptibility. All tree-based ML models achieved good performance, with accuracy values greater than 0.79. The categorical boosting model performed the best than other models to predict flood susceptibility. The flood susceptibility maps showed that more than 16% of the areas of the GBA were classified as having high flood susceptibility, and almost 70% of the historical floods were located in areas with high flood susceptibility. The model interpretation of the summary of Shapley additive explanation values indicated that the influential factors of elevation, population density, and typhoon intensity had a strong influence on flood susceptibility. The obtained spatial flood susceptibilities provide suggestions for flood disaster mitigation in the GBA.