Enhancing the performance of deep learning technique by combining with gradient boosting in rainfall-runoff simulation

Artificial neural networks are widely employed as data mining methods by researchers across various fields, including rainfall-runoff (R-R) statistical modelling. To enhance the performance of these networks, deep learning (DL) neural networks have been developed to improve modelling accuracy. The present study aims to improve the effectiveness of DL networks in enhancing the performance of artificial neural networks via merging with the gradient boosting (GB) technique for daily runoff data forecasting in the river Amu Darya, Uzbekistan. The obtained results showed that the new hybrid proposed model performed exceptionally well, achieving a 16.67% improvement in determination coefficient ( R2) and a 23.18% reduction in root mean square error ( RMSE) during the training phase compared to the single DL model. Moreover, during the verification phase, the hybrid model displayed remarkable performance, demonstrating a 66.67% increase in R 2 and a 50% reduction in RMSE. Furthermore, the hybrid model outperformed the single GB model by a significant margin. During the training phase, the new model showed an 18.18% increase in R 2 and a 25% reduction in RMSE. In the verification phase, it improved by an impressive 75% in R 2 and a 33.33% reduction in RMSE compared to the single GB model. These findings highlight the potential of the hybrid DL-GB model in improving daily runoff data forecasting in the challenging hydrological context of the Amu Darya River basin in Uzbekistan.

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