Short-term prediction of groundwater level using improved random forest regression with a combination of random features

To solve the problem where by the available on-site input data are too scarce to predict the level of groundwater, this paper proposes an algorithm to make this prediction called the canonical correlation forest algorithm with a combination of random features. To assess the effectiveness of the proposed algorithm, groundwater levels and meteorological data for the Daguhe River groundwater source field, in Qingdao, China, were used. First, the results of a comparison among three regressors showed that the proposed algorithm is superior in terms of forecasting variations in groundwater level. Second, the results of experiments were used to show the comparative superiority of the proposed method in terms of training time and complexity of parameter optimization. Third, using the proposed algorithm, the highest prediction accuracy was achieved by employing precipitation P ( t − 2), temperature T ( t ), and groundwater level H ( t ) as the best time lag. This improved random forest regression model yielded higher accuracy in forecasting the variation in groundwater level. The proposed algorithm can also be applied to cases involving low-dimensional data.