Among all the areas impacted by global warming, the glaciers of Karakoram and Indus basin are most severely impacted, causing rapid and chaotic environmental phenomena. Coupled with rapid population growth, seasonal crops irrigation, and urbanization, the water management concern is widespread. Flooding stands as a significant natural calamity, with its mitigation hinging on precise and reliable streamflow predictions. The Upper Indus Basin (UIB) in Pakistan is particularly susceptible to flood events, which have shown an increased frequency in recent years. Due to the diverse topography of the UIB, it can be segmented into various sub-regions, with the Massam region bearing a notable cumulative impact. This research utilizes hydrological and meteorological data from stations across the UIB to investigate seasonal hydro-meteorological fluctuations. A novel approach through a hybrid model is introduced, integrating Convolutional Neural Network (CNN), specifically a Quantile-transformed attention residual Network (QT-ARN). This model leverages data spanning from 1960 to 2012, collected from 17 different locations by the Surface-water hydrology project and the Pakistan Meteorological Department. The efficacy of the model is assessed using statistical metrics and the Nash-Sutcliffe Efficiency coefficient. Findings indicate that decomposition-based approaches surpass purely AI-driven models in forecasting precision. Notably, the QT-ARNN model outperforms other AI models significantly. Validation of these results includes a peak value analysis during the flood-prone months of June to September, where the model demonstrates exceptional accuracy, with a 91.3% success rate. The addition residual inception into the QTARNN enhances the temporal learning in the data enhancing the accuracy by 5.6% in flood mitigation scenarios, reflected in the statistical indices scores between 39.3% to 32.3%. An extended study on the Mueglits river basin, Germany shows the strong generalization in application of the proposed model for the flow/discharge with high fidelity in diverse geographical locations.
Keywords: Deep learning; Upper Indus Basin (UIB); Mueglits River Basin (MRB); Quantile-transformed attention residual Network (QT-ARN); Convolutional Neural Network (CNN); water resource management