Developing flood forecasting techniques at short timescales improve early warning systems to mitigate severe flood risk and facilitate effective emergency response strategies at vulnerable sites. In this study, we develop a hybrid deep learning algorithm, C-GRU, by integrating Convolutional Neural Networks (CNN) with Gated Recurrent Unit (GRU) model and evaluate its effectiveness in forecasting an hourly flood index ( \(SWRI_{24-hr-S}\) ) in five flood-prone, specific study sites in Fiji. The model incorporates statistically significant lagged \(SWRI_{24-hr-S}\) with real-time hourly rainfall measurements obtained from rainfall stations, and comparative analysis is performed against benchmark models: CNN, GRU, Long Short-Term Memory and Random Forest Regression. The proposed model’s outputs comprise the \(SWRI_{24-hr-S}\) predicted at each specific site at a lead time of 1-h. The results demonstrate that the proposed hybrid C-GRU model outperforms all the other models in accurately forecasting \(SWRI_{24-hr-S}\) over a 1-hourly forecast horizon. Across all of the study sites, the proposed model consistently generates the highest r (0.996–0.999) and the lowest RMSE (0.007–0.014) and MAE (0.003–0.004) in the testing phase. The proposed hybrid C-GRU model also achieves the highest Global Performance Index (GPI) values and the largest percentage of forecast errors (FE) ( \(\approx \) 98.9–99.9%) within smaller error brackets (i.e., \(|\hbox {FE}|< 0.05\) ) across all study sites. Using the methodologies developed, we show the practical application of the proposed framework as a decision support system for early flood warning, demonstrating its potential to enhance real-time monitoring and early warning systems with broader application to flood-prone regions.
