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首页> 外文期刊>The Science of the Total Environment >Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models
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Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models

机译：普拉霍瓦河流域（罗马尼亚）上中游地区的洪灾潜力评估。四种混合模型之间的比较方法

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An accurate assessment of Flash-Flood Potential for certain areas is mandatory for the improvement of flash-flood forecast and warnings. The main aim of the present study is represented by the calculation of Flash-Flood Potential Index within the upper and the middle sector of Prahova river catchment (Romania) by using 4 hybrid models: Logistic Regression-Frequency Ratio (LR-FR) model, Logistic Regression-Weights of Evidence (LR-WoE) model, Support Vector Machine-Frequency Ratio (SVM-FR) model and Support Vector Machine-Weights of Evidence (SVM-WoE). The identification of areas affected by torrential phenomena represents the first step performed in the present research. These areas with a total surface of 260 km(2) were divided into training areas (70%) and validating areas (30%). By the mean of Linear Support Vector Machine (LSVM) model, 10 flash-flood conditioning factors were selected and further used for the Flash-Flood Potential assessment. Based on the spatial relationship between areas affected by torrential phenomena and flash-floods conditioning factors characteristics, the FR and WoE coefficients were calculated. In order to be integrated into Logistic Regression and Support Vector Machine (RBF) analysis, these values were standardized. According to the results of the 4 hybrid models used for FFPI calculation, the high and very high Flash-Flood Potential are spread over 33% of the study area. The model performance assessment and results validation were carried out by the mean of the three different methods: i) relative frequency distribution of torrential phenomena pixels within FFPI classes; ii) ROC Curve (Success Rate and Prediction Rate) and AUC value; iii) statistical measures represented by Sensitivity, Specificity and Accuracy. (c) 2018 Elsevier B.V. All rights reserved.
机译：必须准确评估某些区域的洪灾潜力，以改善洪灾预报和警告。本研究的主要目的是通过使用以下4种混合模型计算Prahova流域（罗马尼亚）上中游地区的洪水洪涝潜力指数来表示的：Logistic回归频率比（LR-FR）模型， Logistic回归证据权重（LR-WoE）模型，支持向量机频率比（SVM-FR）模型和支持向量机权重（SVM-WoE）。确定受洪流现象影响的区域代表了本研究的第一步。这些总面积为260 km（2）的区域分为训练区域（70％）和验证区域（30％）。借助线性支持向量机（LSVM）模型，选择了10个快速洪水条件因子，并将其进一步用于快速洪水潜力评估。根据洪涝现象影响区域与山洪暴发条件因素特征之间的空间关系，计算了FR和WoE系数。为了集成到Logistic回归和支持向量机（RBF）分析中，对这些值进行了标准化。根据用于FFPI计算的4种混合模型的结果，高和非常高的泛洪潜力分布在研究区域的33％上。模型性能评估和结果验证是通过三种不同的方法进行的：i）FFPI类内洪流像素的相对频率分布； ii）ROC曲线（成功率和预测率）和AUC值； iii）以灵敏度，特异性和准确性为代表的统计量度。 （c）2018 Elsevier B.V.保留所有权利。

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