Machine Learning Approach Using Cloud Computing and Water Quality Prediction to Reduce Emmisions to the Water Ecosystems

 The aim of the project is to create an intelligent algorithm for predicting the quality and quantity of wastewater entering the wastewater treatment plant. Predicting these data over a period of hours to days will make it possible to design operational measures to reduce the risk of pollution emissions, including simulation scenarios and risk analysis. The algorithm will use cloud-computing and machine learning. Based on an in-depth analysis of available data on wastewater (qualitative and quantitative data, recurring events, ie days of week, season, etc. along with weather forecasts), a neural network capable of predicting the quality of incoming wastewater will be created. This knowledge will make it possible to create a database of scenarios of operational measures at WWTPs.