The "Synthetic Stream Gauges: An LSTM-Based Approach to Enhance River Streamflow Predictions in Unmonitored Segments" project (under review) employs Long Short-Term Memory (LSTM) networks to generate synthetic streamflow data for unmonitored river segments. This innovative approach helps overcome the significant gaps in streamflow data, crucial for effective water resource management, flood prediction, and ecological conservation.
The project aims to provide reliable streamflow data in regions lacking physical gauge networks, facilitating better flood risk management and water resource allocation. By integrating synthetic data with real-world measurements, the model promises to enhance the robustness and applicability of hydrological forecasts. This method stands to transform environmental monitoring and management, offering a cost-effective alternative to extensive physical monitoring infrastructures.