Urban flood forecasting with multi-output neural networks: a physically-based and data-driven approach

Physically based models for spatial flood prediction are time and computationally expensive. Data-driven models, while faster, require large amounts of data for adjustment. This study presents an original methodology combining these two approaches, using a physically-based model (HEC-RAS 2D), adjusted with known events, to generate water depth data at control points and multi-output artificial neural networks (ANNs) for flood forecasting at these points. The performance of the ANN in this research, with application to the urban area of Lages-SC, southern Brazil, resulted in average mean absolute errors of 3.9, 9.8, and 46 cm, with corresponding Nash-Sutcliffe coefficients of 0.99, 0.98, and 0.75 at lead times of 3 h, 8 h, and 20 h, respectively. Multi-output ANNs exhibited greater robustness compared to single-output ANNs for spatial flood prediction. The methodology is suitable for developing models for spatial predictions of urban flooding, with sufficient agility to take necessary measures.