Application of machine learning algorithms for nonlinear system forecasting through analytics — A case study with mining influenced water data

Various techniques have been researched and introduced in water treatment plants to optimise treatment and management processes. This paper presents a solution that can help treatment plants to work more effectively and reach their mine water management goals. Using Python 3.7.1 programming language within an Anaconda 4.11.0 platform, neural networks and regression tree algorithms were compared to find the best performing model after the data had undergone robust data pre-processing and exploratory data analysis statistical techniques. The main aim was to use this best performing model to forecast mining influenced water (MIW) parameters. This approach will help the treatment plant operators in knowing the future MIW chemistry, and they can eventually plan ahead of time what chemicals and methods to use to treat and manage polluted MIW. Westrand mine pool water near Randfontein, South Africa is used as a case study, in which historical data (2016–2021) from shaft № 9 is used to train and test the algorithms. These algorithms included the artificial neural network (ANN), deep neural network (DNN), gradient boosting and random forest regression trees, while the multivariate long short-term memory (LSTM) was used to generate new data for the best performing algorithm. Different data pre-processing approaches were explored, including data interpolation and anomaly detection. These processes were carried out to highlight the most important part of completing a machine learning related project, which is data analytics. Finally, the random forest regression tree algorithm showed the overall best performance and was used to forecast Fe and acidity concentrations of MIW for 60 days. It could be shown that artificial intelligence techniques are capable to optimise and forecast mine water treatment plant parameters, and it is imperative to perform robust statistical analysis on the data before attempting to build forecasting models.