基于BP神经网络的谢尔曼单位线优选算法研究与实践OACSTPCD
Study and Application of Sherman Unit Hydrograph Optimization Algorithm Based on BP Neural Network
洪水预报是防洪减灾的重要技术手段,但传统预报过程受人为经验因素影响较大.将辽宁指数模型、谢尔曼单位线、BP神经网络模型有机结合,构建LNZS-UH-BP模型.以降雨总量、降雨量时段最大、降雨强度、暴雨中心、径流深总量、径流深时段最大、初始蓄水量、单位线洪峰流量、单位线洪峰时段9项指标为输入节点,单位线编号为输出节点,构建9-3-1架构的BP神经网络优选模型,该模型可结合场次降雨特点自动优选单位线进行产汇流计算.以葠窝水库为研究区域,采用历史30场…查看全部>>
Flood forecasting is an important technical means for flood control and disaster reduction,but the traditional forecasting process is greatly influenced by human experience factors.This article combines the Liaoning index model,Sherman unit hydrograph,and BP neural network model together to construct the LNZS-UH-BP model.The 9-3-1 BP neural network optimization model is constructed by taking the total rainfall,the maximum rainfall period,the rainfall intensi…查看全部>>
王惟一
辽宁润中供水有限责任公司,辽宁 沈阳 110003
地球科学
辽宁指数模型谢尔曼单位线BP神经网络LNZS-UH-BP模型葠窝水库
Liaoning index modelSherman unit hydrographBP neural networkLNZS-UH-BP modelShenwo Reservoir
《水力发电》 2024 (8)
5-10,6
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