Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms

Abstract

One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.

Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions

“…Therefore the regression may be mapped into the classification by discretizing the output value [27]. There is much literature on applying supervised classification ML algorithms in different applications like wastewater plant, software, aircraft engine reliability, and COVID-19 effective prediction [28][29][30][31]; as well as there are much literature on using supervised regression ML algorithms in diverse requests like healthcare [32], finance [33], robotics [34], travel [35], the automotive industry [36], and atmospheric corrosion [37].…”