Water quality prediction using SWAT-ANN coupled approach

Abstract

Efficient and accurate prediction of river water quality is challenging due to the complex hydrological and environmental processes affecting their nature. The challenge is even bigger in unmonitored watersheds. Both process- and data-based approaches are utilized for this purpose, with each having its own strengths and weaknesses. The development of a hybrid model can potentially give robust solutions in this regard. To improve the water quality predictions in unmonitored watersheds, we developed a hybrid model by combining a process-based watershed model and artificial neural network (ANN). Combining these two models helped to optimize the calibration and validation process while accounting for the complex hydrological and water quality processes. The developed model was applied to watersheds in the Atlanta metropolitan area, USA, to predict monthly nitrate, ammonium, and phosphate loads. We treated the watersheds as unmonitored and tested the skill of the hybrid model accordingly. The hybrid model had good skills in predicting all three constituents. The model worked especially well for nitrate. As a matter of fact, it even outperformed SWAT models calibrated at each site. This work emphasizes the potential benefits of the proposed hybrid modeling framework for the prediction of water quality parameters in unmonitored watersheds.