| 任明磊,王本德.基于模糊聚类和BP神经网络的流域洪水分类预报研究[J].,2009,(1):121-127 |
| 基于模糊聚类和BP神经网络的流域洪水分类预报研究 |
| Research on classified flood forecast based on fuzzy clustering and BP neural networks |
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| DOI：10.7511/dllgxb200901023 |
| 中文关键词: 洪水预报 分类 BP神经网络 模糊聚类 |
| 英文关键词: flood forecast classification BP neural networks fuzzy clustering |
| 基金项目:国家自然科学基金资助项目(50479056). |
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| 中文摘要: |
| 传统的流域洪水预报大都通过率定一组水文模型参数来寻求一个流域径流形成的一般性或平均化规律，其预报精度需要进一步提高．用模糊聚类ISODATA迭代模型将历史洪水分为若干类型，进行水文预报模型参数的分类调试；并建立BP神经网络分类模型判断实时洪水所属类别，选择其相应类别的模型参数实现流域洪水的分类预报．在辽宁省大伙房水库流域的实际应用表明 此方法不但可以实现洪水实时在线分类而且提高了流域整体洪水预报精度，是一种为水库实时调度提供可靠依据的有效洪水预报方法． |
| 英文摘要: |
| Traditional flood forecast usually tries to find the generic or average disciplinarian of forming runoff in the basin by rating a set of hydrological model parameters, and its forecasting precision needs to be further improved. Firstly, the historical floods were divided into several types by fuzzy clustering ISODATA iterative model, and several sets of hydrological model parameters were debugged separately. Secondly, BP neural networks classified model was established to judge the category of real-time flood, and the model parameters fit to the real-time flood were chosen to realize classified flood forecast. The factual application in Dahuofang reservoir basin shows that this method can realize the classification of real-time flood on-line, improve the forecasting precision integrally, and provide reliable information for real-time operation of the reservoir. |
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