Abstract Machine learning-based data-driven models have achieved great success since their invention. Nowadays, the artificial neural network (ANN)-based machine learning methods have made great progress than ever before, such as the deep learning and reinforcement learning, etc. In this study, we coupled the ANN with the K-nearest neighbor method to propose a novel hybrid machine learning (HML) hydrological model for flood forecast purpose. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the traditional neural network training, a genetic algorithm and Levenberg–Marquardt-based multi-objective training method was also proposed. Real-world applications of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of further applications of the HML hydrological model in flood forecast problems.

中文翻译：

---

用于洪水预测的混合机器学习水文模型

摘要 基于机器学习的数据驱动模型自发明以来取得了巨大成功。如今，基于人工神经网络 (ANN) 的机器学习方法比以往任何时候都取得了长足的进步，例如深度学习和强化学习等。 在本研究中，我们将 ANN 与 K-最近邻方法相结合，提出一种用于洪水预报的新型混合机器学习 (HML) 水文模型。所提出的模型相对于传统神经网络模型的优势在于，由于其特殊设计的模型结构，它可以连续预测流量而不会损失精度。为了克服传统神经网络训练的局部最小值问题，还提出了一种遗传算法和基于Levenberg-Marquardt的多目标训练方法。