Mahdizadeh Gharakhanlou Navid, Perez Liliana
Laboratory of Environmental Geosimulation (LEDGE), Department of Geography, University of Montreal, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, Canada.
Entropy (Basel). 2022 Nov 10;24(11):1630. doi: 10.3390/e24111630.
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.
本研究的主要目的是利用四种机器学习模型,即梯度提升机(GBM)、随机森林(RF)、多层感知器神经网络(MLP-NN)和朴素贝叶斯(NB),预测在RCP2.6(即乐观情景)、RCP4.5(即照常情景)和RCP8.5(即悲观情景)这三种气候变化情景下当前和未来的洪水易发性。该研究针对加拿大的两个流域展开,即不列颠哥伦比亚省的下尼古拉河和魁北克省的卢普河。使用了三种统计指标来验证模型:接收者操作特征曲线、品质因数和F1分数。研究结果表明,RF模型在提供洪水易发性地图(FSM)方面具有最高的准确性。此外,所提供的FSM表明,下尼古拉河流域比卢普河流域更易发生洪水。按照RCP4.5情景,卢普河流域在2050年和2080年洪水易发性类别的面积百分比与2020年和2050年相比分别变化如下:极低=-1.68%,低=-5.82%,中等=+6.19%,高=+0.71%,极高=+0.6%,以及极低=-1.61%,低=+2.98%,中等=-3.49%,高=+1.29%,极高=+0.83%。同样,在下尼古拉河流域,2020年至2050年以及2050年至2080年之间的变化分别为:极低=-0.38%,低=-0.81%,中等=-0.95%,高=+1.72%,极高=+0.42%,以及极低=-1.31%,低=-1.35%,中等=-1.81%,高=+2.37%,极高=+2.1%。气候变化对未来洪水易发地区的影响表明,在两个流域的预测中,被指定为高度和极易发生洪水的区域都在增加。本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时间和各种气候变化情景下洪水易发性的新颖见解。