Assessing the Accuracy of Contemporaneous Time Series and Neural Network Models in Modeling Rainfall-Runoff (Case study: Nazloochaei Catchment)

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ZEYNALI M.J.; Khashei Siyooki A.

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Journal: Journal of Water and Soil Conservation Year:2018 | Volume:25 | Issue:2 Page(s): 315-321

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Title

Assessing the Accuracy of Contemporaneous Time Series and Neural Network Models in Modeling Rainfall-Runoff (Case study: Nazloochaei Catchment)

Author(s)

ZEYNALI M.J. | Khashei Siyooki A. | Issue Writer Certificate

Keywords

Artificial neural networksQ2

Multivariate modelsQ2

Rainfall-runoff modelingQ1

Time seriesQ1

Abstract

Background and Objectives: Rainfall-runoff modeling is an essential process and very complicated phenomena that is necessary for proper reservoir system operation and successful water resources planning and management. There are different methods like conceptual and numerical methods for modeling of this process. Theoretically, a system modeling required explicit mathematical relationships between inputs and outputs variables. Developing such explicit model is very difficult because of unknown relationship between variables and substantial uncertainty of variables. So far performance models such as neural networks, Multivariate models with auto moving average is studied for modeling the rainfall-runoff. So, in this study CARMA and ANN models studied in Rainfall-runoff modeling. Materials and Methods: In this research, the multivariate contemporaneous autoregressive moving average (CARMA) models and Artificial neural networks (ANN) were evaluated to Rainfall-runoff modeling. We define 3 scenario for ANN model. In order to use CARMA and ANN models total annual precipitation and runoff Time series in the period of 1975-2015 as for Nazloochaei the catchment area, in 44° 49' in latitude and 37° 40' longitude in the province of West Azerbaijan was used. At first, we checked the data in terms of randomness, trend and Homogeneity by run test, Mann-Kendall test and Wilcoxon test. And then we separated data in two group. One group including 80 presents of data for training and 20 percent of data for validation was assigned. Performance criteria that used, was root mean square error, Nash-Sutcliffe and correlation coefficient. Results: The results indicated that the multivariate contemporaneous autoregressive moving average model had the efficiency accuracy more than Artificial neural networks model, because root mean square error in CARMA model was equal 7. 7 and that was in ANN model 9. 50 m3/s. Also, CARMA model according to the Nash-Sutcliffe criteria and R2 equal to 0. 41 and 0. 54 had better performance than the ANN model. However, the value of these performance criteria in ANN model was equal 0. 45 and 0. 80. So CARMA model has more Accuracy than ANN model in Rainfall-runoff modeling. Conclusion: According to the obtained results, using multivariate ARMA models caused to decrease in model error up to 18 percentages. So CARMA model had suitable performance in compare with ANN model, and this subject show the importance of to consider Random component in modeling.

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APA: Copy

ZEYNALI, M.J., & Khashei Siyooki, A.. (2018). Assessing the Accuracy of Contemporaneous Time Series and Neural Network Models in Modeling Rainfall-Runoff (Case study: Nazloochaei Catchment). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 25(2 ), 315-321. SID. https://sid.ir/paper/156334/en

Vancouver: Copy

ZEYNALI M.J., Khashei Siyooki A.. Assessing the Accuracy of Contemporaneous Time Series and Neural Network Models in Modeling Rainfall-Runoff (Case study: Nazloochaei Catchment). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2018;25(2 ):315-321. Available from: https://sid.ir/paper/156334/en

IEEE: Copy

M.J. ZEYNALI, and A. Khashei Siyooki, “Assessing the Accuracy of Contemporaneous Time Series and Neural Network Models in Modeling Rainfall-Runoff (Case study: Nazloochaei Catchment),” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 25, no. 2 , pp. 315–321, 2018, [Online]. Available: https://sid.ir/paper/156334/en

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