Flood Hazard Assessment in Australian Tropical Cyclone-Prone Regions

IF 3 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Climate Pub Date : 2023-11-13 DOI:10.3390/cli11110229
Michael Kaspi, Yuriy Kuleshov
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Abstract

This study investigated tropical cyclone (TC)-induced flooding in coastal regions of Australia due to the impact of TC Debbie in 2017 utilising a differential evolution-optimised random forest to model flood susceptibility in the region of Bowen, Airlie Beach, and Mackay in North Queensland. Model performance was evaluated using a receiver operating characteristic curve, which showed an area under the curve of 0.925 and an overall accuracy score of 80%. The important flood-influencing factors (FIFs) were investigated using both feature importance scores and the SHapely Additive exPlanations method (SHAP), creating a flood hazard map of the region and a map of SHAP contributions. It was found that the elevation, slope, and normalised difference vegetation index were the most important FIFs overall. However, in some regions, the distance to the river and the stream power index dominated for a similar flood hazard susceptibility outcome. Validation using SHAP to test the physical reasoning of the model confirmed the reliability of the flood hazard map. This study shows that explainable artificial intelligence allows for improved interpretation of model predictions, assisting decision-makers in better understanding machine learning-based flood hazard assessments and ultimately aiding in mitigating adverse impacts of flooding in coastal regions affected by TCs.
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澳大利亚热带气旋易发地区的洪水灾害评估
本研究调查了2017年受热带气旋黛比(TC Debbie)影响,澳大利亚沿海地区发生的热带气旋(TC)引发的洪水,利用差分进化优化随机森林模拟了北昆士兰Bowen、Airlie Beach和Mackay地区的洪水易感性。采用受试者工作特征曲线评价模型性能,曲线下面积为0.925,总体准确率为80%。利用特征重要性评分和SHapely加性解释法(SHAP)对重要洪水影响因子(FIFs)进行了研究,绘制了该地区的洪水灾害图和SHAP贡献图。高程、坡度和归一化植被指数是最重要的ifs指标。然而,在一些地区,与河流的距离和河流功率指数主导了类似的洪水灾害易感性结果。利用SHAP对模型的物理推理进行验证,证实了洪水灾害图的可靠性。这项研究表明,可解释的人工智能可以改进对模型预测的解释,帮助决策者更好地理解基于机器学习的洪水灾害评估,并最终帮助减轻受tc影响的沿海地区洪水的不利影响。
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来源期刊
Climate
Climate Earth and Planetary Sciences-Atmospheric Science
CiteScore
5.50
自引率
5.40%
发文量
172
审稿时长
11 weeks
期刊介绍: Climate is an independent, international and multi-disciplinary open access journal focusing on climate processes of the earth, covering all scales and involving modelling and observation methods. The scope of Climate includes: Global climate Regional climate Urban climate Multiscale climate Polar climate Tropical climate Climate downscaling Climate process and sensitivity studies Climate dynamics Climate variability (Interseasonal, interannual to decadal) Feedbacks between local, regional, and global climate change Anthropogenic climate change Climate and monsoon Cloud and precipitation predictions Past, present, and projected climate change Hydroclimate.
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