Comparative insights into short-term streamflow forecasting in navigating urban flood risks

Accurate short-term streamflow forecasting is essential for mitigating urban flood risks and ensuring sustainable water resource management. This study presents a comparative analysis of advanced data-driven techniques for streamflow prediction in the Gandak River basin, India, evaluating their efficacy in handling rapid hydrological fluctuations. Three sophisticated machine learning models – random forest (RF), extreme gradient boosting (XGBoost) and long short-term memory (LSTM) networks – are employed to forecast streamflow across lead times ranging from 1 to 30 days. The findings reveal that all models exhibit strong predictive capabilities for 1-day lead times, achieving high coefficients of determination (R²): 0.976 (LSTM), 0.970 (RF) and 0.964 (XGBoost). However, predictive accuracy diminishes significantly as the forecasting horizon extends. By the 30-day lead time, the LSTM network consistently outperforms the tree-based models (R² = 0.759 compared to RF’s 0.564 and XGBoost’s 0.569). LSTM’s superior performance for medium- to long-term forecasts is attributed to its advanced architecture, which effectively captures long-term dependencies and non-linear sequential patterns in hydrological time-series data. This study demonstrates the added value of deep learning over traditional ensemble methods for extended forecasting, offering actionable insights into optimising predictive frameworks for urban flood preparedness.