Improvement of mid- to long-term runoff forecasting based on physical causes: application in Nenjiang basin, China

Li, Hongyan; Tian, Lin; Wu, Yue; Xie, Ming

doi:10.1080/02626667.2013.833664

Cited by 7 publications

(2 citation statements)

References 17 publications

(10 reference statements)

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“…The back propagation neural network (BPNN) is one of the most widely used neural network models, and it is a multi-layer feedforward network trained based on the error back propagation algorithm. BPNN can learn and store a large number of input-output mode mapping relations, and users can obtain relatively satisfactory prediction results without having to understand the mathematical equations of this mapping relation in advance [8]. BPNN continuously adjusts the weights and thresholds of the network through back propagation to achieve the least sum of square error.…”

Section: Back Propagation Neural Network (Bpnn)mentioning

confidence: 99%

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Song

Liu

Sun

et al. 2020

Water

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Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.

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Section: Back Propagation Neural Network (Bpnn)mentioning

confidence: 99%

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Song

Liu

Sun

et al. 2020

Water

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“…The zeroing disturbance of an independent variable is introduced to deduce the change of a dependent variable, so that we can define the sensitivity of the dependent variable to the independent variable and conduct global sensitivity analysis based on multiple factors coupling. Global sensitivity analysis considers the interaction between the variables and therefore is more suitable for most of the practical problems 39 , 40 .…”

Section: Methodsmentioning

confidence: 99%

Identification of key influence factors and an empirical formula for spring snowmelt-runoff: A case study in mid-temperate zone of northeast China

Tian

et al. 2018

Sci Rep

Self Cite

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Because of the unique climate characteristics, the runoff law in mid-temperate zone is very different from other regions in spring. Accurate runoff simulation and forecasting is of great importance to spring flood control and efficient use of water resources. Baishan reservoir is located in the upper Second Songhua River Basin in Northeast China, where snowmelt is an important source of runoff that contributes to the water supply. This study utilized long-term hydrometeorological data, in the contributing area of Bashan reservoir, to investigate factors and time-lag effects on spring snowmelt and to establish a snowmelt-runoff model. Daily precipitation, temperature, and wind data were collected from three meteorological stations in this region from 1987–2016. Daily runoff into the Baishan reservoir was selected for the same period. The snowmelt period was identified from March 23 to May 4 through baseflow segmentation with the Eckhardt recursive digital filtering method combined with statistical analyses. A global sensitivity analysis, based on the back propagation neural network method, was used to identify daily radiation, wind speed, mean temperature, and precipitation as the main factors affecting snowmelt runoff. Daily radiation, precipitation, and mean temperature factors had a two-day lag effect. Based on these factors, an empirical snowmelt runoff model was established by genetic algorithm (GAS) to estimate the snowmelt runoff in this area. The model showed an acceptable performance with coefficient of determination (R2) of 73.6%, relative error (Re) of 25.10%, and Nash-Sutcliffe efficiency coefficient (NSE) of 66.2% in the calibration period of 1987–2010, while reasonable performance with R2 of 62.3%, Re of 27.2%, and NSE of 46.0% was also achieved during the 2011–2016 validation period.

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Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting

Zhao

Chen

2015

Water Resour Manage

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Improvement of mid- to long-term runoff forecasting based on physical causes: application in Nenjiang basin, China

Cited by 7 publications

References 17 publications

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Identification of key influence factors and an empirical formula for spring snowmelt-runoff: A case study in mid-temperate zone of northeast China

Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting

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