Akademik Veri Yönetim Sistemi
Water Resources Management, 2025 (SCI-Expanded, Scopus)
Monitoring the flow rates of rivers and appropriately managing their basins can be possible by developing effective forecasting methods. Therefore, streamflow prediction can be a crucial indicator for determining the future conditions of water resources. This study presents a novel approach for accurate hydrological time series forecasting. The proposed Adaptive Hybrid-Graph Convolution Attention Network, referred to as AH-GCAN-LSTM, integrates Long Short-Term Memory (LSTM) to capture temporal dependencies and Graph Convolutional Networks (GCN) to model spatial relationships between hydrological stations. To assess the robustness of the proposed method, we compared it to other algorithms such as FFO (Fire Fly Algorithm), PSO (Particle Swarm Optimization) and GA (Genetic Algorithm). A multi-head attention mechanism dynamically enhances the model’s ability to focus on crucial spatial and temporal features. Additionally, genetic algorithms are used to optimize the model’s parameters, further improving its predictive accuracy. In the study, statistical measurement metrics such as RMSE, MAE, R2 and KGE were used. Results demonstrate significant improvements in forecasting accuracy compared to traditional and state-of-the-art models, with reduced error rates and enhanced predictive performance across the Ulucami, Kaptanpaşa, and Cumhuriyet datasets. Specifically, the proposed AH-GCAN-LSTM model achieved RMSE values of 0.0295, 0.0894, and 0.0769; MAE values of 0.0258, 0.0308, and 0.0333; and R2 scores of 0.9874, 0.9863, and 0.9866, respectively. Additionally, the model attained high NSE values—0.9905, 0.9965, and 0.9964—demonstrating consistent robustness. Compared to GRU, Multi GRU-LSTM Attention, and A-LSTM, AH-GCAN-LSTM outperformed all baselines across all metrics. Genetic Algorithm-based optimization further improved performance, reducing RMSE to 0.02 at Ulucami and 0.06 at Cumhuriyet stations. These results confirm the model’s strong ability to capture complex spatial and temporal dependencies, establishing it as a highly effective tool for hydrological forecasting and water resource management.