Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data

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.

Automated summary

Accurate estimation of streamflow is crucial in water resources engineering. This study compared GMDH-NN, DENFIS, and MARS methods for monthly streamflow prediction in a mountainous basin in Pakistan. Results indicated that DENFIS model performed best at Kalam Station, while MARS model outperformed at Chakdara Station.