Unleashing the power of AI: revolutionizing runoff prediction beyond NRCS-CN method

Predicting runoff is vital for effectively planning and managing water resources within a watershed or river basin. This research aims to compare the effectiveness of two distinct approaches in predicting daily runoff within the Koyna River basin in India from 1999 to 2011. The approaches examined are an artificial intelligence-based data-driven model, specifically an artificial neural network (ANN), and a conceptual-based model, the Natural Resource Conservation Service Curve Number (NRCS-CN) method. The ANN model employs a data-driven approach that utilizes historical runoff data to train the model, allowing it to capture nonlinear relationships and complexities in runoff dynamics. In contrast, the NRCS-CN method uses a conceptual-based approach, relying on empirical relationships and soil cover complex data to estimate runoff. The performance of both models was evaluated using the coefficient of determination (R²) as a key metric. The study highlights a significant difference in predictive performance between the two methodologies. The NRCS-CN method achieved an R² of 0.37, whereas the ANN model significantly improved the predictive accuracy, achieving an R² of 0.88. This substantial increase demonstrates the ANN model’s superior ability to capture the complexities of daily runoff dynamics compared to the NRCS-CN method. In conclusion, the findings strongly advocate for the efficacy of the data-driven ANN model over the conceptual-based NRCS-CN model for daily runoff prediction. The superior performance of the ANN model provides valuable insights for enhancing water resource management through advanced artificial intelligence techniques. These results suggest that integrating AI-driven models can significantly improve the accuracy and reliability of runoff predictions, thereby supporting more effective water resource planning and management.