Abstract
A reliable model to predict the changes in the water levels in a river is crucial for better planning to mitigate any risk associated with flooding. In this study, six different Machine Learning (ML) algorithms were developed to predict the river's water level, on a daily basis based on collected data from 1990 to 2019 which were used to train and test the proposed models. Different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed models with other models. The comparison of several data-driven regression methods indicate that the exponential Gaussian Process Regression (GPR) model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The GPR model was then used to predict the water level after sorting the data based on 10 days maximum and minimum values of the water level, and the results proved the success of this model in catching the extremes of the water levels. In addition to that, based on two uncertainty indicators, it was concluded that the proposed model, the GPR, was capable of predicting the water level of the river with high precision and less uncertainty where the computed using the 95% prediction uncertainty (95PPU) and the d-factor were found to be equal to 98.276 and 0.000525, respectively. The findings of this study show the efficacy of the GPR model in capturing the changes in the water level in a river. Due to the importance of the water level of a river being an parameter for flood monitoring, this technique is likely beneficial to the design of the mitigation strategies for future flooding events.