To improve the accuracy of runoff forecasting,an uncertain multiple linear regression(UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variabl...To improve the accuracy of runoff forecasting,an uncertain multiple linear regression(UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming(ITSP) model is used for crop planting structure optimization(CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.展开更多
Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling techniq...Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.展开更多
The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the su...The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the surface of Hongze Lake. The influence of reservoirs and gates on flood forecasting was considered in a practical and simple way. With a one-day time step, the linear and non-linear Muskingum method was used for channel flood routing, and the least-square regression model was used for real-time correction in flood forecasting. Representative historical data were collected for the model calibration. The hydrological model parameters for each sub-basin were calibrated individually, so the parameters of the Xin＇anjiang model were different for different sub-basins. This flood forecasting system was used in the real-time simulation of the large flood in 2005 and the results are satisfactory when compared with measured data from the flood.展开更多
Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature ...Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature and precipitation changes on annual river runoff process. The model was calibrated and verified by using BP neural network with observed meteorological and runoff data from Daiying Hydrological Station in the Chaohe River of Hebei Province in 1956–2000. Compared with auto-regression model,linear multi-regression model and linear mixed regression model,NMR can improve forecasting precision remarkably. Therefore,the simulation of climate change scenarios was carried out by NMR. The results show that the nonlinear mixed regression model can simulate annual river runoff well.展开更多
径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上...径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。展开更多
Runoff and its evolution, based on hydrometeorological data from surface measurement stations, are analyzed for the upper reaches of the Yellow River above Tangnag. Some mathematical statistical models, for example, P...Runoff and its evolution, based on hydrometeorological data from surface measurement stations, are analyzed for the upper reaches of the Yellow River above Tangnag. Some mathematical statistical models, for example, Period Extrapolation-Gradual Regression Model, Grey Topology Forecast Model and Box-Jinkins Model, are applied in predicting changing trends on the runoff. The analysis indicates that the runoff volume in the upper Yellow River above Tangnag is ending a period of extended minimum flows. Increasing runoff is expected in the coming years.展开更多
径流曲线数模型(Soil Conservation Service Curve Number Model,简称SCS-CN模型)可以利用降雨资料估算径流,对水资源合理配置和山洪灾害预警具有重要意义,因为其方便计算、参数简单,而被广泛应用。目前标准SCS-CN模型在山区小流域的适...径流曲线数模型(Soil Conservation Service Curve Number Model,简称SCS-CN模型)可以利用降雨资料估算径流,对水资源合理配置和山洪灾害预警具有重要意义,因为其方便计算、参数简单,而被广泛应用。目前标准SCS-CN模型在山区小流域的适用性欠佳,因此需要对模型参数进行优化以提高预测精度。本文以湖南省螺岭桥流域为例,根据实测降雨径流资料优化径流曲线数CN(Curve Number)查算表,并利用步长优化参数算法研究初损率对模型精度的影响,将优化模型的方法应用于湖南省凤凰小流域,验证该优化方法的可靠性。结果分析表明:与标准SCS-CN模型相比,优化后的SCS-CN模型效率系数NSE从0.576提升至0.813,决定系数R^(2)为0.858。将模型优化方法验证于气候地形条件相似的凤凰流域,模型NSE值提高117%。通过预测径流深与实测径流深比较,优化模型模拟精度较为理想,对湖南省山区小流域场次降雨产流预报有一定的参考意义。展开更多
基金supported by the National Key Research and Development Plan of China (2016YFC0400207)the National Natural Science Foundation of China (51439006)the National High Technology Research and Development Program of China (2013AA102904)
文摘To improve the accuracy of runoff forecasting,an uncertain multiple linear regression(UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming(ITSP) model is used for crop planting structure optimization(CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.
文摘Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.
基金supported by the National Natural Science Foundation of China (Grant No. 50479017)the Program for Changjiang Scholars and Innovative Research Teams in Universities (Grant No. IRT071)
文摘The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the surface of Hongze Lake. The influence of reservoirs and gates on flood forecasting was considered in a practical and simple way. With a one-day time step, the linear and non-linear Muskingum method was used for channel flood routing, and the least-square regression model was used for real-time correction in flood forecasting. Representative historical data were collected for the model calibration. The hydrological model parameters for each sub-basin were calibrated individually, so the parameters of the Xin＇anjiang model were different for different sub-basins. This flood forecasting system was used in the real-time simulation of the large flood in 2005 and the results are satisfactory when compared with measured data from the flood.
基金Under the auspices of National Natural Science Foundation of China (No. 50809004)
文摘Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature and precipitation changes on annual river runoff process. The model was calibrated and verified by using BP neural network with observed meteorological and runoff data from Daiying Hydrological Station in the Chaohe River of Hebei Province in 1956–2000. Compared with auto-regression model,linear multi-regression model and linear mixed regression model,NMR can improve forecasting precision remarkably. Therefore,the simulation of climate change scenarios was carried out by NMR. The results show that the nonlinear mixed regression model can simulate annual river runoff well.
文摘径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。
基金National Natural Science Foundation of China, No. 49731030 Knowledge Innovation Project of CAS, No. 210016
文摘Runoff and its evolution, based on hydrometeorological data from surface measurement stations, are analyzed for the upper reaches of the Yellow River above Tangnag. Some mathematical statistical models, for example, Period Extrapolation-Gradual Regression Model, Grey Topology Forecast Model and Box-Jinkins Model, are applied in predicting changing trends on the runoff. The analysis indicates that the runoff volume in the upper Yellow River above Tangnag is ending a period of extended minimum flows. Increasing runoff is expected in the coming years.
文摘径流曲线数模型(Soil Conservation Service Curve Number Model,简称SCS-CN模型)可以利用降雨资料估算径流,对水资源合理配置和山洪灾害预警具有重要意义,因为其方便计算、参数简单,而被广泛应用。目前标准SCS-CN模型在山区小流域的适用性欠佳,因此需要对模型参数进行优化以提高预测精度。本文以湖南省螺岭桥流域为例,根据实测降雨径流资料优化径流曲线数CN(Curve Number)查算表,并利用步长优化参数算法研究初损率对模型精度的影响,将优化模型的方法应用于湖南省凤凰小流域,验证该优化方法的可靠性。结果分析表明:与标准SCS-CN模型相比,优化后的SCS-CN模型效率系数NSE从0.576提升至0.813,决定系数R^(2)为0.858。将模型优化方法验证于气候地形条件相似的凤凰流域,模型NSE值提高117%。通过预测径流深与实测径流深比较,优化模型模拟精度较为理想,对湖南省山区小流域场次降雨产流预报有一定的参考意义。
