Application of moving windows autoregressive quadratic model in runoff forecast

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NETL
IEEE

外文摘要：This paper describes a novel method to mid-long term runoff prediction using moving windows autoregressive quadratic model which combines autoregressive quadratic model and moving windows method to improve prediction capability of natural runoff。 The parameters of the model are determined in light of the joints of half-sine function， self-adaptive optimization， smoothly moving windows and generalized likelihood uncertainty estimation。 The application shows that the model can not only improve prediction capability but keep robust， and shows that the model has simpler structure and less parameter than artificial neural networks model， and avoids locally minimal point and excess study， etc。 Therefore， the moving windows autoregressive quadratic model is a promising tool for mid-long term runoff forecast。

外文关键词：

geophysics computinghydrologymoving average processesneural netsoptimisationriversChinaHongshanzui gauging stationManas RiverXinjiangartificial neural network modelhalf-sine functionmoving window autoregressive quadratic modelmoving window methodrunoff forecastself-adaptive optimizationmid-long term runoff predictionmoving windowsquadratic autoregressiveself-adaptive

作者：

Z. Ren、Z.C. Hao

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作者单位：

Hydro-Lab., HHU, Nanjing, China

会议名称：

International Conference on Industrial Mechatronics and Automation;ICIMA 2009

会议地点：

Chengdu(CN);Chengdu(CN)

会议母体文献：

Industrial Mechatronics and Automation, 2009. ICIMA 2009

页码：

200-203

出版时间：

2009

DOI：

10.1109/ICIMA.2009.5156595