小水电站流域径流量的评估方法

Evaluation Method for Watershed Ｒunoff of Small Hydropower Stations

摘要:

现今对于河流径流量的预测方法有许多模型，但各种模型都有其优劣，而且预测的精度都有差异。以韶关的 南水河作为研究对象，利用南水 1978—2015 年径流量的数据，分别使用灰色系统理论、基于遗传算法的 BP 神经网络 以及支持向量机 3 种方法对南水河年径流量预测的模型。结果表明: GA － BP 神经网络的预测精度为 84． 30% ，但其 拟合精度不高; 灰色系统预测以及拟合的精度分别为 86． 70% 和 84． 24% ; 用支持向量机对南水河年径流量的预测，无 论在拟合精度为 95． 67% ，还是预测精度为 99% 上，比灰色预测和 GA － BP 神经网络都要高。因此，支持向量机可以 很好地应用在南水河年径流量的预测研究中。

Abstract:

Nowadays，there are many models for river runoff prediction，but each model has its advantages and disadvantages， and there is difference in prediction accuracy． Taking Nanshui river in Shaoguan as the research object and using the runoff data of Nanshui river from 1978 to 2015，the grey system theory，BP neural network based on genetic algorithm ( GA － BP) and support vector machine( SVM) are used to predict the annual runoff of Nanshui river espectively． The results show that the prediction accuracy of BP neural network based on genetic algorithm is 84． 30% ，but its fitting accuracy is not high; the prediction accuracy and the fitting accuracy of grey system are 86． 70% and 84． 24% respectively; whether the fitting accuracy is 95． 67% or the prediction accuracy is 99% ，the prediction of annual runoff of Nanshui river by SVM is better than that by grey prediction and GA － BP neural network． Therefore，SVM can be well applied to the prediction of annual runoff in Nanshui Ｒiver．