半湿润流域洪水预报实时校正方法比较
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摘要
为了提高新安江模型在半湿润流域的洪水预报精度,选择K最近邻(KNN)算法、传统的误差自回归(AR)方法、反馈模拟方法3种实时校正方法,以陕西省陈河流域为试验对象进行洪水预报。以洪峰相对误差和纳什效率系数为评价指标,分析对比3种方法的校正效果。结果表明:3种校正方法均能提高预报纳什效率系数,其中反馈模拟最优,AR、KNN效果次之;反馈模拟对洪峰误差校正相比于KNN算法在短预见期内更为精确,两者均能减小AR法在洪峰误差校正上的不足;加入历史样本的KNN算法在洪峰误差校正上效果优于反馈模拟,可有效提高洪水预报精度。
To provide more reliable simulations and forecasts using the Xinanjiang model in the semi-humid watersheds, this study introduced three real-time correction methods into the flood forecasting, respectively, including the K-nearest neighbor algorithm(the KNN method), the traditional error autoregression method(the AR method) and the simulating feedback method. The Chenhe Basin, in Shaanxi Province, was selected as the experimental basin. Considering the relative error of flood peak and the coefficient of Nash-Sutcliffe efficiency as evaluation indicators, this study analyzed the results of three correction methods. The results show that all three kinds of correction methods can improve the coefficient of Nash-Sutcliffe efficiency and the simulating feedback method was optimal on the Nash-Sutcliffe efficiency coefficient, while the AR method and the KNN method were the second best. The simulating feedback method allow a remarkable improvement compared with the KNN method in a short forecast period, and both of them can effectively avoid the defect of the AR method in terms of the error correction of flood peak. The results also indicate the KNN method with historical samples yielded better results than the simulation of feedback method on the error correction of flood peak, which can effectively improve the accuracy of flood forecasting.
To provide more reliable simulations and forecasts using the Xinanjiang model in the semi-humid watersheds, this study introduced three real-time correction methods into the flood forecasting, respectively, including the K-nearest neighbor algorithm(the KNN method), the traditional error autoregression method(the AR method) and the simulating feedback method. The Chenhe Basin, in Shaanxi Province, was selected as the experimental basin. Considering the relative error of flood peak and the coefficient of Nash-Sutcliffe efficiency as evaluation indicators, this study analyzed the results of three correction methods. The results show that all three kinds of correction methods can improve the coefficient of Nash-Sutcliffe efficiency and the simulating feedback method was optimal on the Nash-Sutcliffe efficiency coefficient, while the AR method and the KNN method were the second best. The simulating feedback method allow a remarkable improvement compared with the KNN method in a short forecast period, and both of them can effectively avoid the defect of the AR method in terms of the error correction of flood peak. The results also indicate the KNN method with historical samples yielded better results than the simulation of feedback method on the error correction of flood peak, which can effectively improve the accuracy of flood forecasting.
引文
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[18] 李致家,梁世强,霍文博,等.淮河上中游复杂流域洪水预报[J].河海大学学报(自然科学版),2019,47(1):1-6.(LI Zhijia,LIANG Shiqiang,HUO Wenbo,et al.Study on the flood forecasting in complex basins of upper and middle reaches of Huaihe River[J].Journal of Hohai University (Natural Sciences),2019,47(1):1-6.(in Chinese))
[19] 李致家.现代水文模拟与预报技术[M].南京:河海大学出版社,2010.
[20] 芮孝芳.水文学前沿科学问题之我见[J].水利水电科技进展,2015,35(5):95-102.(RUI Xiaofang.Discussion of some frontier problems in hydrology[J].Advances in Science and Technology of Water Resources,2015,35(5):95-102.(in Chinese))
[21] 童冰星,李致家,温娅惠,等.基于地貌单位线的汇流模型在陈河流域的构建与应用[J].水力发电,2017,43(10):19-22.(TONG Bingxing,LI Zhijia,WEN Yahui,et al.Construction and application of geomorphologic Instantaneous unit hydrograph confluence model in Chenhe Catchment[J].Water Power,2017,43(10):19-22.(in Chinese))
[22] KARLSSON M,YAKOWITZ S.Rainfall-runoff forecasting methods,old and new[J].Stochastic Hydrology & Hydraulics,1987,1(4):303-318.
[ 2 ] KALMAN R E.A new approach to linear filtering and prediction problems[J].Journal of Basic Engineering Transactions,1960,82(1):35-45.
[ 3 ] EVENSEN G.The ensemble Kalman filter:theoretical formulation and practical implementation[J].Ocean Dynamics,2003,53(4):343-367.
[ 4 ] LJUNG L.Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems[J].IEEE Transactions on Automatic Control,1979,24(1):36-50.
[ 5 ] HSU K L,GUPTA H V,SOROOSHIAN S.Artificial neural network modeling of the rainfall-runoff process[J].Water Resources Research,1995,31(10):2517-2530.
[ 6 ] XIONG Lihua,O′CONNOR K M.Comparison of four updating models for real-time river flow forecasting[J].International Association of Scientific Hydrology Bulletin,2002,47(4):621-639.
[ 7 ] XIONG Lihua,O′CONNOR K M,GUO Shenglian.Comparison of three updating schemes using artificial neural network in flow forecasting[J].Hydrology & Earth System Sciences & Discussions,2004,8(2):247-255.
[ 8 ] AL-ALAWI S M,ABDUL-WAHAB S A,BAKHEIT C S.Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone[J].Environmental Modelling & Software,2008,23(4):396-403.
[ 9 ] AKSOY H,DAHAMSHEH A.Artificial neural network models for forecasting monthly precipitation in Jordan[J].Stochastic Environmental Research & Risk Assessment,2009,23(7):917-931.
[10] RAFTERY A E,GNEITING T,BALABDAOUI F,et al.Using Bayesian model averaging to calibrate forecast ensembles[J].Monthly Weather Review,2005,133(5):1155-1174.
[11] 芮孝芳.流域水文模型研究中的若干问题[J].水科学进展,1997,8(1):94-98.(RUI Xiaofang.Some problems in research of watershed hydrological model[J].Advances in Water Science,1997,8(1):94-98.(in Chinese))
[12] 翟家瑞.常用水文预报算法和计算程序[M].郑州:黄河水利出版社,1995.
[13] 阚光远,李致家,刘志雨,等.改进的神经网络模型在水文模拟中的应用[J].河海大学学报(自然科学版),2013,41(4):294-299.(KAN Guangyuan,LI Zhijia,LIU Zhiyu,et al.An improved neural network model and its application to hydrological simulation[J].Journal of Hohai University (Natural Sciences),2013,41(4):294-299.(in Chinese))
[14] 刘开磊,姚成,李致家,等.水动力学模型实时校正方法对比[J].河海大学学报(自然科学版),2014,42(2):124-129.(LIU Kailei,YAO Cheng,LI Zhijia,et al.Comparison of real-time correction methods of hydrodynamic model[J].Journal of Hohai University (Natural Sciences),2014,42(2):124-129.(in Chinese))
[15] 韩通,李致家,刘开磊,等.山区小流域洪水预报实时校正研究[J].河海大学学报(自然科学版),2015,43(3):208-214.(HAN Tong,LI Zhijia,LIU Kailei,et al.Research on real-time correction method of flood forecasting in small mountain watershed[J].Journal of Hohai University(Natural Sciences),2015,43(3):208-214.(in Chinese))
[16] 赵人俊.流域水文模拟:新安江模型与陕北模型[M].北京:水利电力出版社,1984.
[17] 霍文博,朱跃龙,李致家,等.新安江模型和支持向量机模型实时洪水预报应用比较[J].河海大学学报(自然科学版),2018,46(4):283-289.(HUO Wenbo,ZHU Yuelong,LI Zhijia,et al.Comparison of Xin’anjiang model and Support Vector Machine model in the application of real-time flood forecasting[J].Journal of Hohai University (Natural Sciences),2018,46(4):283-289.(in Chinese))
[18] 李致家,梁世强,霍文博,等.淮河上中游复杂流域洪水预报[J].河海大学学报(自然科学版),2019,47(1):1-6.(LI Zhijia,LIANG Shiqiang,HUO Wenbo,et al.Study on the flood forecasting in complex basins of upper and middle reaches of Huaihe River[J].Journal of Hohai University (Natural Sciences),2019,47(1):1-6.(in Chinese))
[19] 李致家.现代水文模拟与预报技术[M].南京:河海大学出版社,2010.
[20] 芮孝芳.水文学前沿科学问题之我见[J].水利水电科技进展,2015,35(5):95-102.(RUI Xiaofang.Discussion of some frontier problems in hydrology[J].Advances in Science and Technology of Water Resources,2015,35(5):95-102.(in Chinese))
[21] 童冰星,李致家,温娅惠,等.基于地貌单位线的汇流模型在陈河流域的构建与应用[J].水力发电,2017,43(10):19-22.(TONG Bingxing,LI Zhijia,WEN Yahui,et al.Construction and application of geomorphologic Instantaneous unit hydrograph confluence model in Chenhe Catchment[J].Water Power,2017,43(10):19-22.(in Chinese))
[22] KARLSSON M,YAKOWITZ S.Rainfall-runoff forecasting methods,old and new[J].Stochastic Hydrology & Hydraulics,1987,1(4):303-318.
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