Hybrid CNN–GRU model for hourly flood forecasting index: case studies from the Fiji islands

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Abstract 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.