A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks

Abstract

Climate change is driving worsening flood events worldwide. In this study, a hybrid approach based on a combination of the optimization of a hydrodynamic model and an error correction modeling that exploit different aspects of the physical system is proposed to improve the forecasting accuracy of flood water levels. In the parameter optimization procedure for the hydrodynamic model, Manning’s roughness coefficients were estimated by considering their spatial distribution and temporal variation in unsteady flow conditions. In the following error correction procedure, the systematic errors of the optimized hydrodynamic model were captured by combining the input variable selection method using partial mutual information (PMI) and artificial neural networks (ANNs), and therefore, complementary information provided by the data was achieved. The developed ANNs were used to analyze the potential non-linear relationships between the considered state variables and simulation errors to predict systematic errors. To assess the hybrid forecasting approach (hydrodynamic model with an ANN-based error correction model), performances of the hydrodynamic model, two ANN-based water-level forecasting models (ANN1 and ANN2), and the hybrid model were compared. Regarding input candidates, ANN1 considers the historical observations only, and ANN2 considers not only the historical observations that used in ANN1 but also the prescribed boundary conditions required for the hydrodynamic forecast model. As a result, the hybrid model significantly improved the forecasting accuracy of flood water levels compared to individual models, which indicates that the hybrid model is able to take advantage of complementary strengths of both the hydrodynamic model and the ANN model. The optimization of the hydrodynamic model allowing spatially and temporally variable parameters estimated water levels with acceptable accuracy. Furthermore, the use of PMI-based input variable selection and optimized ANNs as error correction models for different sites were proven to successfully predict simulation errors in the hydrodynamic model. Hence, the parameter optimization of the hydrodynamic model coupled with error correction modeling for water level forecasting can be used to provide accurate information for flood management.

Review of Machine Learning Methods for River Flood Routing

“…However, one of the main limitations of ML models is that the trained models are difficult to generalize due to their limited prediction ability when the inputs of the model go beyond the data used to train them. ML models can be highly sensitive to the input data [10,14]. The effects of training data on the performance of ML models have not been fully studied, as mentioned by [86].…”

“…Li and Jun [10] hybridized a hydrodynamic model based on the Saint-Venant equations with ANNs to improve the accuracy of flood forecasting for the Han River, South Korea. This study applied ANNs to correct the errors of the hydrodynamic model using the observed discharge and water levels, and outputs of the hydrodynamic model.…”

“…Coulibaly et al [32] showed that an ANN with only one hidden layer is appropriate to solve hydrological problems. Therefore, many studies in the hydrological field used one hidden layer to estimate the nonlinear relationships [10,30]. The number of hidden nodes is usually defined by performing a trial-and-error method [30].…”

“…Various physical characteristics and boundary conditions are required to be determined to construct physically based models, which require in-depth (extensive) knowledge of the physical process. Widely used physically based models involve the Muskingum model [1][2][3] and the hydrodynamic model based on Saint-Venant equations, which are solved by various numerical methods [4][5][6][7][8][9][10].…”