Application of Machine Learning Techniques in Forecasting Groundwater Levels in the Grootfontein Aquifer

Abstract

In this paper, we attempt to provide a data driven solution to model groundwater levels in the Grootfontein Aquifer in the North West Province of South Africa by testing several predictive models. Groundwater plays a crucial role in supplying water to a significant part of the population for agricultural, industrial, environmental and/or domestic use. Recent advancements in data analytics, and the analysis of large data sets has allowed the production of powerful predictive models. Five different data driven techniques namely, support vector regression, gradient boosting trees, decision trees, random forest regression and multilayer feed-forward neural network techniques were applied to predict groundwater levels. Modelling was carried out for four boreholes located in the Grootfontein dolomite aquifer considering discharge, rainfall and temperature as model inputs. Five site specific models were developed for each borehole. Model performance was evaluated using coefficient of determination and root mean squared error. Comparison of goodness of fit revealed that data driven methods can indeed capture the trend of water level fluctuations in the aquifer sufficiently with the GB algorithm performing better than other algorithms in both the training and verification stages. Whilst the models performed adequately when predicting groundwater level on a monthly basis for 36 months, further investigation is needed towards determining their efficacy in longer term projections to assist in the decision making process of sustainable groundwater use. This paper provides the following contributions: (a) a ranking of the attributes according to their mutual information (MI); (b) a reference for model selection; and (c) a predictive model to forecast groundwater levels in the Grootfontein aquifer.