Floods have become one of the most dangerous and frequent natural disasters. Most rivers are characterised by compound cross-sections that are usually contain vegetation. The ability to simulate water surface profiles (WSPs) in vegetated rivers quickly and accurately is crucial in flood forecasting operations. The aim of this study was to develop a low-cost and practical tool for predicting the WSP in compound channels with vegetated floodplains. In particular, artificial neural network (ANN) and support vector machine (SVM) techniques were used to devise a model for the prediction of the WSP in an experimental channel. Two approaches were employed: the first was based on the use of non-dimensional data and the second used dimensional data. The performance of the prediction methods was determined using a ten-fold cross-validation approach. Comparative results revealed that the SVM algorithm outperformed the ANN and regression models. The performance of the SVM model using dimensional data (correlation coefficient of 0.99 ± 0.005 and mean absolute error of 0.0019 ± 0.0002) was shown to be marginally better than the model using dimensionless data. Sensitivity analysis also indicated that the relative discharge and relative depth played the most important roles in estimating the WSP.

中文翻译：

---

植被泛滥平原复合河道水面剖面预测

洪水已成为最危险、最频繁的自然灾害之一。大多数河流的特点是复合断面，通常含有植被。快速准确地模拟植被河流中的水面剖面 (WSP) 的能力对于洪水预报作业至关重要。本研究的目的是开发一种低成本且实用的工具来预测具有植被泛滥平原的复合河道中的 WSP。特别是，人工神经网络 (ANN) 和支持向量机 (SVM) 技术被用来设计一个模型来预测实验通道中的 WSP。采用了两种方法：第一种方法基于使用无量纲数据，第二种方法基于使用量纲数据。预测方法的性能是使用十倍交叉验证方法确定的。比较结果表明，SVM 算法优于 ANN 和回归模型。使用有量纲数据的 SVM 模型（相关系数为 0.99 ± 0.005，平均绝对误差为 0.0019 ± 0.0002）的性能略好于使用无量纲数据的模型。敏感性分析还表明，相对流量和相对深度在估算 WSP 中发挥着最重要的作用。