Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models.

IF 2
Navid Mahdizadeh Gharakhanlou, Liliana Perez
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引用次数: 4

Abstract

The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

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当前和未来洪水易感性的空间预测:利用机器学习模型研究气候变化对洪水易感性的影响。
采用梯度增强机(GBM)、随机森林(RF)、多层感知器神经网络(MLP-NN)和Naïve贝叶斯(NB) 4种机器学习模型,对RCP2.6(即乐观)、RCP4.5(即照常)和RCP8.5(即悲观)3种气候变化情景下的当前和未来洪水易感性进行预测。这项研究是在加拿大的两个流域进行的,即下尼古拉河,不列颠哥伦比亚省和卢普,QC。使用三个统计指标来验证模型:受试者工作特征曲线,优点图和f1得分。结果表明,RF模型在提供洪水敏感性图(FSMs)方面具有最高的准确性。此外,提供的FSMs表明,下尼古拉河流域比卢普流域更容易发生洪水。在RCP4.5情景下,与2020年和2050年相比,2050年和2080年Loup流域洪水易感等级面积百分比的变化幅度分别为:极低= -1.68%、低= -5.82%、中= +6.19%、高= +0.71%、极高= +0.6%、极低= -1.61%、低= +2.98%、中= -3.49%、高= +1.29%、极高= +0.83%。Nicola河下游流域2020 ~ 2050年和2050 ~ 2080年的变化分别为:Very Low = -0.38%、Low = -0.81%、Moderate = -0.95%、High = +1.72%、Very High = +0.42%、Very Low = -1.31%、Low = -1.35%、Moderate = -1.81%、High = +2.37%、Very High = +2.1%。气候变化对未来洪水易发地区的影响表明,在这两个流域的预测中,被指定为高度和非常易受洪水影响的地区都在增长。这项研究的主要贡献在于它提供了关于不列颠哥伦比亚省和魁北克省流域在不同气候变化情景下随时间变化的洪水易感性的新见解。
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