Abstract

The present study aims to evaluate the susceptibility to floods in the river basin of Buzau in Romania through the following 6 machine learning models: Support Vector Machine (SVM), J48 decision tree, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), Artificial Neural Network (ANN) and Alternating Decision Tree (ADT). In the first stage of the study, an inventory of the areas affected by floods was made in the study area, and a number of 205 flood points were identified. Further, 12 flood predictors were selected to be used for final susceptibility mapping. The six models' training was performed by using 70% of the total flood points that have been associated with the values of flood predictors. The highest accuracy (0.973) was obtained by the RF model, while J48 had the lowest performance (0.825). Besides, by classifying flood predictors' values in flood and non-flood pixels, the six flood susceptibility maps were made. High and very high flood susceptibility values cover between 17.71% (MLP) and 27.93% (ANFIS) of the study area. The validation of the results, performed using the ROC Curve, shows that the most accurate flood susceptibility values are also assigned to the RF model (AUC = 0.996).

摘要

本研究旨在通过以下 6 种机器学习模型评估罗马尼亚 Buzau 流域洪水的易感性：支持向量机（SVM）、J48 决策树、自适应神经模糊推理系统（ANFIS）、随机森林（ RF)、人工神经网络 (ANN) 和交替决策树 (ADT)。在研究的第一阶段，对研究区域内受洪水影响的区域进行了清查，确定了205个洪水点。此外，还选择了 12 个洪水预测器用于最终的敏感性绘图。六个模型的训练是通过使用与洪水预测值相关联的总洪水点的 70% 进行的。RF 模型获得了最高的精度 (0.973)，而 J48 的性能最低 (0.825)。除了，通过对洪水和非洪水像素中的洪水预测值进行分类，制作了六幅洪水敏感性图。高和非常高的洪水敏感性值覆盖了研究区域的 17.71% (MLP) 和 27.93% (ANFIS)。使用 ROC 曲线进行的结果验证表明，RF 模型也分配了最准确的洪水敏感性值 (AUC = 0.996)。