Comparison of machine learning models for flood forecasting in the Mahanadi River Basin, India

Abstract

Developing accurate flood forecasting models is necessary for flood control, water resources and management in the Mahanadi River Basin. In this study, convolutional neural network (CNN) is integrated with random forest (RF) and support vector regression (SVR) for making a hybrid model (CNN–RF and CNN–SVR) where CNN is used as feature extraction technique while RF and SVR are used as forecasting models. These hybrid models are compared with RF, SVR, and artificial neural network (ANN). The influence of training–testing data division on the performance of hybrid models has been tested. Hyperparameter sensitivity analyses are performed for forecasting models to select the best value of hyperparameters and to exclude the nonsensitive hyperparameters. Two hydrological stations (Kantamal and Kesinga) are selected as case studies. Results indicated that CNN–RF model performs better than other models for both stations. In addition, it is found that CNN has improved the accuracy of RF and SVR models for flood forecasting. The results of the training–testing division show that both models’ performance is better at 50–50% data division. Validation results show that both models are not overfitting or underfitting. Results demonstrate that CNN–RF model can be used as a potential model for flood forecasting in river basins. HIGHLIGHTS CNN-based hybrid machine learning models are developed for flood forecasting and are compared with other ML models.; The impact of training–testing is analyzed and is selected for the best performance of models.; Four input models are tested for better input combinations for flood forecasting.; Sensitivity analysis of developed models is done for identifying the sensitive, insensitive and most sensitive parameters of models.;

Keywords

• artificial neural network
• convolutional neural network
• forecasting
• machine learning
• random forest
• support vector machine