Mid and Long-Term Hydrological Forecasting Using Optimal Combined Model
Abstract: This paper applies six models, including the autoregressive model, the seasonal autoregressive model, the threshold autoregressive model, the nearest neighbor bootstrap regressive model, the artificial neural network model and the support vector machine model into the mid and long-term hydrological forecasting. Based on Feilaixia reservoir project, the results show that the artificial neural network model is able to time series very well. The support vector machine model has the powerful ability of not only the simulation but also the forecasting. The results of those models were combined by the optimal combined forecasting model. The mean absolute error and the mean square error are selected as the measurements. Relying on the merits of each single model, the results of the optimal combined forecasting model work very well and are very well in robustness.
文章引用: 刘 玥 , 刘 攀 , 黄焕坤 , 李立平 , 虞云飞 , 张安标 (2013) 中长期水文预报的最优组合模型研究。 水资源研究， 2， 377-381. doi: 10.12677/JWRR.2013.26053
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