Adnan, Rana Muhammad and Liang, Zhongmin and Parmar, Kulwinder Singh and Soni, Kirti and Kisi, Ozgur (2021) Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data. Neural Computing and Applications, 33 (7). pp. 2853-2871. ISSN 0941-0643

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Abstract

Accurate estimation of streamflow has a vital importance in water resources engineering, management and planning. In the present study, the abilities of group method of data handling-neural networks (GMDH-NN), dynamic evolving neural-fuzzy inference system (DENFIS) and multivariate adaptive regression spline (MARS) methods are investigated for monthly streamflow prediction. Precipitation, temperature and streamflows from Kalam and Chakdara stations at Swat River basin (mountainous basin), Pakistan, are used as inputs to the applied models in the form of different input scenarios, and models' performances are evaluated on the basis of root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE) and combined accuracy (CA) indexes. Test results of the Kalam Station show that the DENFIS model provides more accurate prediction results in comparison of GMDH-NN and MARS models with the lowest RMSE (18.9 m(3)/s), MAE (13.1 m(3)/s), CA (10.6 m(3)/s) and the highest NSE (0.941). For the Chakdara Station, the MARS outperforms the GMDH-NN and DENFIS models with the lowest RMSE (47.5 m(3)/s), MAE (31.6 m(3)/s), CA (26.1 m(3)/s) and the highest NSE (0.905). Periodicity (month number of the year) effect on models' accuracies in predicting monthly streamflow is also examined. Obtained results demonstrate that the periodicity improves the models' accuracies in general but not necessarily in every case. In addition, the results also show that the monthly streamflow could be successfully predicted using only precipitation and temperature variables as inputs.

Item Type:	Article
Additional Information:	Copyright for this article belongs to M/s Springer.
Subjects:	Computer Science Computer Science > Cybernetics, Artificial Intelligence and Robotics
Divisions:	UNSPECIFIED
Depositing User:	Mr. Yogesh Joshi
Date Deposited:	01 Apr 2022 09:53
Last Modified:	01 Apr 2022 09:53
URI:	http://npl.csircentral.net/id/eprint/4819

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