Using machine learning to emulate the hydrodynamic model for flood inundation modelling

Floods are one of the most devastating natural disasters in the world causing loss of human lives and property across the world. These losses can be minimized by accurate prediction of floods well in advance. However, 2D hydrodynamic models which are used for flood inundation modelling require high computational time and hence are unsuitable for development of real-time flood monitoring system in most cases. Therefore, a surrogate machine learning model named XGBoost Regressor (XBGR) is developed for flood inundation modelling. The developed model overcomes the constraint of high computational time required by 2D hydrodynamic models. The XGBR is developed to predict maximum flood depth map and is evaluated with the LISFLOOD-FP hydrodynamic model. The training data for the XGBR model is generated using the LISFLOOD-FP model. The surrogate model is trained on 21 flood events, tested on 4 and validated for 1 flood event. For better development of the surrogate model, physical characteristics of the study area are considered in the form of nine indices referred here as topographic variables along with the flood characteristic variables. However, to refrain the XGBR model from overfitting and decrease the training time, a feed forward feature selection method is used to select the best predictive topographic variables. Four topographic variables are selected after which there is no significant improvement in the model was found. Number of trees and learning rate parameters of XGBR model are parameterized which are having highest impact on the model performance. Mean absolute error (MAE) and root mean square error (RMSE) are used for evaluating model accuracy. For testing period, the average MAE and RMSE are 0.433 m and 0.780 m, respectively and for the validation event MAE and RMSE are 0.595 m and 0.960 m respectively. For evaluating the accuracy of the surrogate model on flood inundation extent, F1 score is used which is the harmonic mean of precision and recall. The F1 score is 0.908 for the testing events and is 0.931 for validation events. The higher value of F1 score (>0.9) indicates good accuracy of the XGBR model when validated using the hydrodynamic model.