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Sciences in Cold and Arid Regions ›› 2018, Vol. 10 ›› Issue (6): 468–481.doi: 10.3724/SP.J.1226.2018.00468

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  • 收稿日期:2018-03-09 接受日期:2018-09-05 出版日期:2018-12-01 发布日期:2018-12-29

Simulation and prediction of monthly accumulated runoff, based on several neural network models under poor data availability

JianPing Qian1,JianPing Zhao1,Yi Liu2,3,XinLong Feng1,DongWei Gui2,3,*()   

  1. 1 College of Mathematics and System Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China
    2 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang 830011, China
    3 Cele National Station of Observation and Research for Desert–Grassland Ecosystem, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China
  • Received:2018-03-09 Accepted:2018-09-05 Online:2018-12-01 Published:2018-12-29
  • Contact: DongWei Gui E-mail:guidwei@ms.xjb.ac.cn
  • Supported by:
    This work was financially supported by the regional collaborative innovation project for Xinjiang Uygur Autonomous Region (Shanghai cooperation organization science and technology partnership project) (2017E01029), the "Western Light" program of the Chinese Academy of Sciences (2017-XBQNXZ-B-016), the National Natural Science Foundation of China (41601595, U1603343, 41471031), and the State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences (G2018-02-08).

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Abstract:

Most previous research on areas with abundant rainfall shows that simulations using rainfall-runoff modes have a very high prediction accuracy and applicability when using a back-propagation (BP), feed-forward, multilayer perceptron artificial neural network (ANN). However, in runoff areas with relatively low rainfall or a dry climate, more studies are needed. In these areas—of which oasis-plain areas are a particularly good example—the existence and development of runoff depends largely on that which is generated from alpine regions. Quantitative analysis of the uncertainty of runoff simulation under climate change is the key to improving the utilization and management of water resources in arid areas. Therefore, in this context, three kinds of BP feed-forward, three-layer ANNs with similar structure were chosen as models in this paper. Taking the oasis–plain region traverse by the Qira River Basin in Xinjiang, China, as the research area, the monthly accumulated runoff of the Qira River in the next month was simulated and predicted. The results showed that the training precision of a compact wavelet neural network is low; but from the forecasting results, it could be concluded that the training algorithm can better reflect the whole law of samples. The traditional artificial neural network (TANN) model and radial basis-function neural network (RBFNN) model showed higher accuracy in the training and prediction stage. However, the TANN model, more sensitive to the selection of input variables, requires a large number of numerical simulations to determine the appropriate input variables and the number of hidden-layer neurons. Hence, The RBFNN model is more suitable for the study of such problems. And it can be extended to other similar research arid-oasis areas on the southern edge of the Kunlun Mountains and provides a reference for sustainable water-resource management of arid-oasis areas.

Key words: oasis, artificial neural network, radial basis function, wavelet function, runoff simulation

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Variable Training Testing
min max mean Sd CSX min max mean Sd CSX
Runoff 53.6 6,797.8 1,086.2 1,446.7 1.73 46.3 5,080.9 958.5 1,245.9 1.47
Precipitation 0.0 34.0 3.1 5.8 2.73 0.0 15.3 2.1 3.2 2.10
Temperature ?8.5 27.1 12.3 10.6 ?0.30 ?8.9 27.5 13.4 10.9 ?0.40
Evaporation 3.7 503.1 233.8 137.3 ?0.02 20.2 464.9 240.8 141.2 ?0.08

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Model type Model No. Input variables Number of
hidden neurons 		Training Testing
R2 NSE PBLAS R2 NSE PBLAS
TANN A1 1p1e1r1c1t 7 0.955 0.971 0.29 0.009 ?0.016 16.60
TANN A2 2p1e1r1c1t 7 0.967 0.979 1.01 0.580 0.592 ?27.86
TANN A3 3p1e1r1c1t 7 0.976 0.985 1.53 0.664 0.663 ?16.11
TANN A4 4p1e1r1c1t 7 0.979 0.986 1.61 0.724 0.696 ?13.41
CWNN A5 1p1e1r1c1t 7 0.852 0.906 0.77 0.417 0.252 ?7.82
CWNN A6 2p1e1r1c1t 7 0.876 0.920 2.27 0.676 0.665 ?0.10
CWNN A7 3p1e1r1c1t 7 0.906 0.940 ?1.34 0.770 0.764 ?6.21
CWNN A8 4p1e1r1c1t 7 0.890 0.930 1.32 0.788 0.839 ?6.21
RBFNN A9 1p1e1r1c1t – 0.947 0.966 ?0.71 0.031 ?0.290 18.26
RBFNN A10 2p1e1r1c1t – 0.946 0.965 0.24 0.627 0.654 ?9.41
RBFNN A11 3p1e1r1c1t – 0.946 0.965 0.00 0.705 0.756 ?2.71
RBFNN A12 4p1e1r1c1t – 0.946 0.966 ?0.01 0.847 0.880 ?6.72

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Model No. Input variables Explaining variable
1 1p1t1r P(t-1), t, R(t-1)
2 2p1t P(t-1), P(t-2), t
3 2p1t1c P(t-1), P(t-2), t, T(t-1)
4 3p1t P(t-1), P(t-2), P(t-3), t
5 3p1t1r P(t-1), P(t-2), P(t-3), t, R(t-1)
6 3p1t1c P(t-1), P(t-2), P(t-3), t, T(t-1)
7 4p1t P(t-1), P(t-2), P(t-3), P(t-4), t
8 4p1t1r P(t-1), P(t-2), P(t-3), P(t-4), t, R(t-1)

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Model No. Input variables Number of
hidden neurons 		Training Testing
R2 NSE PBLAS R2 NSE PBLAS
B1 1p1t1r 6 0.895 0.933 0.08 0.860 0.870 ?17.27
B2 2p1t 7 0.891 0.931 0.29 0.897 0.924 ?12.05
B3 2p1t1c 6 0.940 0.962 1.43 0.886 0.920 ?6.41
B4 3p1t 4 0.903 0.938 ?0.09 0.901 0.933 ?9.71
B5 3p1t1r 6 0.955 0.971 0.41 0.839 0.894 ?3.56
B6 3p1t1c 5 0.935 0.958 0.70 0.872 0.880 ?16.26
B7 4p1t 5 0.923 0.951 2.79 0.835 0.853 ?17.70
B8 4p1t1r 3 0.903 0.938 ?0.26 0.883 0.922 ?7.98

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Model No. Input variables Number of
hidden neurons 		Training Testing
R2 NSE PBLAS R2 NSE PBLAS
C1 1p1t1r 5 0.776 0.857 1.49 0.440 0.297 ?13.40
C2 2p1t 9 0.884 0.926 0.15 0.898 0.883 ?10.49
C3 2p1t1c 6 0.880 0.923 ?0.09 0.896 0.890 ?8.72
C4 3p1t 6 0.882 0.925 0.37 0.889 0.860 ?12.29
C5 3p1t1r 7 0.887 0.928 0.35 0.877 0.868 ?6.53
C6 3p1t1c 5 0.869 0.916 0.39 0.890 0.883 ?8.16
C7 4p1t 7 0.871 0.918 0.23 0.901 0.886 ?8.89
C8 4p1t1r 8 0.901 0.937 1.21 0.874 0.853 7.19

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Model No. Input variables Training Testing
R2 NSE PBLAS R2 NSE PBLAS
D1 1p1t1r 0.891 0.930 0.14 0.432 0.557 ?3.52
D2 2p1t 0.894 0.933 ?0.21 0.892 0.916 ?14.83
D3 2p1t1c 0.910 0.942 ?0.12 0.870 0.889 ?13.20
D4 3p1t 0.924 0.952 ?0.87 0.870 0.909 ?12.19
D5 3p1t1r 0.963 0.976 ?1.50 0.813 0.872 ?3.76
D6 3p1t1c 0.935 0.959 0.26 0.811 0.828 ?21.30
D7 4p1t 0.919 0.948 ?0.01 0.887 0.877 ?19.87
D8 4p1t1r 0.918 0.948 ?0.02 0.906 0.916 ?15.52

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