Long-range forecasting of suspended sediment

被引:10
作者
Pektas, A. O. [1 ]
Cigizoglu, H. K. [2 ]
机构
[1] Bahcesehir Univ, Civil Engn Dept, Istanbul, Turkey
[2] Istanbul Tech Univ, Civil Engn Dept, Istanbul, Turkey
来源
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES | 2017年 / 62卷 / 14期
关键词
suspended sediment; forecasting; artificial neural networks (ANN); multiple linear regression (MLR); autoregressive integrated moving average (ARIMA) model; ARTIFICIAL NEURAL-NETWORKS; PREDICTION; RIVER; MODELS; SIMULATION; LOAD;
D O I
10.1080/02626667.2017.1383607
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
This study examines the employment of two methods, multiple linear regression (MLR) and an artificial neural network (ANN), for multistep ahead forecasting of suspended sediment. The autoregressive integrated moving average (ARIMA) model is considered for one-step ahead forecasting of sediment series in order to provide a comparison with the MLR and ANN methods. For one-and two-step ahead forecasting, the ANN model performance is superior to that of the MLR model. For longer ranges, MLR models provide better accuracy, but there is an important assumption violation. The Durbin-Watson statistics of the MLR models show a noticeable decrease from 1.3 to 0.5, indicating that the residuals are not dependent over time. The scatterplots of the three methods (MLR, ARIMA and ANN) for one-step ahead forecasting for the validation period illustrate close fits with the regression line, with the ANN configuration having a slightly higher R-2 value.
引用
收藏
页码:2415 / 2425
页数:11
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