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BP神经网络在贵州喀斯特山区径流预报中的应用
引用本文:高岩.BP神经网络在贵州喀斯特山区径流预报中的应用[J].地下水,2012(2):63-65.
作者姓名:高岩
作者单位:贵州大学资源与环境工程学院
基金项目:贵州大学自然科学青年科研基金项目[贵大自青基合字(2009)073号];贵州工业大学科研项目[校科合基字(2002年)304号]
摘    要:以贵州六冲河、倒天河流域为例建立喀斯特山区径流预报BP神经网络模型。六冲河流域以七星关站丰水期流量过程为输出数据,以丰水期降雨过程、出口断面前期流量过程、蒸发量作为输入数据,倒天河流域以徐家屯站丰水期流量过程为输出因子,丰水期降雨过程、前期流量过程作为输入因子。预报结果确定性系数DC值分别为0.538、0.420。结果表明将蒸发量作为输入数据、流域面积比较大模型预报精度较大。

关 键 词:BP神经网络  喀斯特山区  径流预报

Application of BP Neural Network to Runoff Forecasting in Karst Mountainous Area in Guizhou
GAO Yan.Application of BP Neural Network to Runoff Forecasting in Karst Mountainous Area in Guizhou[J].Groundwater,2012(2):63-65.
Authors:GAO Yan
Affiliation:GAO Yan(College of Resources and Environment,Guizhou University,Guiyang,Guizhou 550003)
Abstract:Taking Liuchong River basin and Daotian River basin of Guizhou province as examples.The paper establishes the runoff forecasting model in Karst mountainous area.In Liuchong River basin we establish the model by taking Qixingguan station’s data of runoff process during high water period as the outputs and taking the data of its rainfall process and earlier period runoff and evaporation as the inputs.In Daotian River basin we establish the model by taking Xujiatun station’s data of runoff process during high water period as the outputs and taking the data of its rainfall process and earlier period runoff as the inputs.The deterministic coefficient of the results is 0.538 and 0.420.The result shows it whill improve the precision while taking the evaporation as input and the area of the basin is biger.
Keywords:BP neural network  the karst mountainous area and runoff forecasting
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