SENSING WATER POLLUTION IN THE KAFUE RIVER USING CLOUD COMPUTING AND MACHINE LEARNING

Abstract

Clean water and sanitation are the sixth goal under the UN Sustainable Development Goals. However, reports have shown that over 129 countries are not on track to reach this goal by 2030. Besides the lack of basin management, countries are behind on monitoring of the water bodies. Water pollution affects the livelihood of people living in the catchment area of the river especially the people living in the rural areas and the animals that are dependent on that water. People who live in rural areas do not have the privilege of a piped water network that has treated water. Currently, in Zambia, water is monitored once every quarter and so, this leaves the water unmonitored for most of the time. This research proposed the development of a model based on IoT, Cloud Computing and AI for data collection and monitoring, and developed a prototype based on this model which uses machine learning to predict the quality of water. A water monitoring device was built using sensors, an Arduino and a Raspberry pi. The sensors used measured pH, temperature, electrical conductivity, total dissolved solids and turbidity. An Artificial Neural Network with one hidden layer was used to predict the Water Quality Index. This index was based off the National Sanitation Foundation Water Quality Index (NSF-WQI). The results of the model showed that it had an R2 score of 0.953, MAE and MSE of 0.835 and 1.280 respectively. These results support the use of an ANN in the predicting WQI