基于改进的神经网络与支持向量机的小流域日径流量预测研究

数据驱动水文模型可以在不考虑复杂物理过程的情况下,实现对数据种类较少的小流域日径流量的准确预测。本研究基于安徽省黄山市月潭水文监测站点2009-2012年的日径流量监测数据,分别构建粒子群寻优算法改进的神经网络(PSO-BPNN)以及支持向量机(PSO-SVM)模型。通过进行不同形式的模型结果比较发现,两类模型均有较好的拟合能力及泛化能力,其中基于三日流量数据的(PSO-SVM)模型具有最优模拟结果,可以考虑用于月潭流域日径流量的预测,实现流域内水资源的合理配置以及相关灾害的预防。

Prediction of daily runoff in a small watershed based on improved neural network and support vector machine (SVM)

Data-driven hydrological model can realize the accurate prediction for daily runoff in small watershed with less data types without considering the complex physical processes.Based on daily runoff monitoring data from Yuetan hydrological monitoring sites , in Huangshan of Anhui Province from 2009 to 2012,the paper built particle swarm optimization algorithm improved neural network (PSO-BPNN) and support vector machine (PSO-SVM) model.By comparing the results from different types of model, it discovered that both of the two models have good fitted ability and generalization ability, and PSO-SVM model based on three-day runoff data has the best simulation results and it can be used to predict daily runoff of Yuetan Basin and realize the rational allocation of water resources in the basin as well as the early prevention of related disasters.