## 2 Related Work
### Deep Learning in Hydrology  
The growing integration of data-driven modelling in environmental sciences has demonstrated considerable success across a wide range of applications, including soil property estimation [13; 14], crop quality assessment [15], and nutrient management modeling [16]. Recent research [10; 17; 18; 19; 20] suggests that neural networks have the potential to overcome the limitations of traditional modeling approaches and provide a promising alternative to streamflow forecasting. Deep learning approaches have shown remarkable potential in capturing complex hydrological patterns, though they lack the physical consistency and interpretability of traditional models. Table 1 summarizes these prior contributions in terms of methodology, data, metrics, and the specific gaps our hybrid framework addresses. Recent literature shows Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Convolutional Neural Networks (CNNs) as the most widely adopted deep learning techniques for streamflow forecasting. LSTM performed best in small basins with well-distributed rainfall stations, while the CNN and ConvLSTM (a spatiotemporal extension of LSTM) were more effective in larger basins with higher streamflow [21]. Enhancing LSTM with Kalman filters significantly improved long-term daily forecasting and flood prediction accuracy [22]. A complex model combining first-order differencing, feedforward neural networks, and LSTM (DIFF-FFNN-LSTM) effectively addressed non-stationarity in time series data and outperformed conventional machine learning and statistical models [23]. In another comparison, deeper LSTM architectures (S-LSTM), bidirectional LSTMs (Bi-LSTM), and GRUs were tested against simpler models like MLP, revealing that model depth had less impact than climatic variability and time series characteristics [24]. GRU models also achieved the highest accuracy in hourly forecasts across two Spanish basins, especially when future rainfall data were included, while hybrid models like CNN-RNN did not offer further improvements [25]. Building on these foundational studies, recent work has explored the application of newer state-of-the-art architectures such as the Temporal Fusion Transformer (TFT) [30] for streamflow and hydrological forecasting due to its ability to model multi-horizon temporal dependencies while offering interpretability. [PERSON] and [PERSON] [27] were among the first to test the TFT globally across 2610 basins, demonstrating that it outperformed both LSTM and standard Transformer models in streamflow forecasting by effectively combining recurrence and attention mechanisms. The TFT consistently yielded higher performance metrics (e.g., KGE = 0.704) across diverse geographies, latitudes, and basin sizes. In a regional case study, [PERSON] et al. [26] applied the TFT to predict sewer manhole overflows during flash floods, showing that even with a single sensor and simple filling class data, the TFT achieved lower peak and volume errors than CNN, LSTM, or DA-RNN. [PERSON] et al. [31] proposed a hybrid TFT model integrated with Ensemble Empirical Mode Decomposition (EEMD) for multi-step forecasting of karst spring discharge. Their selective EEMD-TFT model significantly outperformed LSTM and standard TFT models, with an RMSE value as low as 0.0255 m\({}^{3}\)/s and strong interpretability regarding short- and long-term hydrological signals. Similarly, [PERSON] et al. [32] used the TFT for hourly hydropower forecasting in a run-of-the-river plant in Portugal, illustrating its suitability for operational scheduling with probabilistic outputs. Lastly, [PERSON] et al. [33] extended the TFT framework to forecast lake water levels and assess environmental water availability for the Poyang and Dongting Lakes in China. With coefficients of determination exceeding 0.98 and Kappa coefficients above 0.90, their work confirmed TFT's ability to couple prediction and environmental assessments for lake management.