Water pollution reduction for sustainable urban development using machine learning techniques

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Water quality is affected by increased urbanization as pollutants produced in the urban environment settle and contaminate water, and there is an increase in competition of water among cities, industries, agriculture, etc. The quality and quantity of water are affected by alterations in the microclimate, water dynamics, geomorphology, ecology, and biogeochemistry. As more pavements get created, it becomes increasingly difficult for water to soak into the ground and this causes a decrease in the water table. Impervious structures like streets and roofs when washed with rain deposit excessive pollutants in water bodies. The overall increase in water pollution is a potential health hazard for humans and aquatic life. Hence it is necessary to take adequate measures for addressing the water pollution issue that may potentially arise due to increased urbanization. In this study, we tackle the issue using two approaches. The first approach deals with analyzing the water quality to determine its potability using fifteen different types of machine learning techniques like random forests, decision trees, support vector machines, artificial neural networks, etc. The model has been evaluated using metrics such as precision, recall, accuracy, and F-1 score. The second approach deals with identifying marine litter from beaches in many parts of the world using machine learning algorithms. We also explore the different types of beach environments and the type of litter that is found in different locations using extensive exploratory analysis. Both approaches can be used for ensuring sustainable urban development by reducing water pollution.