Harnessing Machine Learning in the Water Sector to Accelerate Sustainable Development Goals (SDGs)

Various machine learning (ML) techniques have been recently employed to optimize the wastewater treatment (WWT) plants’ operational conditions, e.g., medium pH, retention time, reactor temperature, and food-to-biomass ratio. These techniques include artificial neural network (ANN), fuzzy logic (FL), and adaptive neuro-fuzzy inference system (ANFIS), and they have been used to predict WWT performances toward pollutant removal. As such, the ML applications aim at maintaining innovative WWT technologies that align with the principles (e.g., environmental, economic, and social pillars) of sustainable development goals (SDGs). Using artificial intelligence to enhance WWT and ensure the availability of clean water resources assists in protecting water bodies, further preventing water pollution, and maintaining ecological balance (SDG#6). It can also create employment opportunities in the wastewater management sector, support economic growth, and provide livelihoods for local communities (SDG#8). This approach also helps protect aquatic ecosystems by removing contaminants from wastewater and preventing their discharge into water bodies, further contributing to the conservation of marine life and the preservation of biodiversity (SDG#14). Other benefits, such as reducing the carbon footprint (SDG#13), avoiding the spread of waterborne diseases (SDG#3), and supporting the development of sustainable cities and communities (SDG#11), can also be achieved by using computational models to enhance the urban water utility networks and infrastructure. By addressing WWT challenges, ML applications promote financial growth, environmental sustainability, and the well-being of communities, contributing to a more sustainable and inclusive future. In summary, various financial, environmental, and social-related SDGs would be fulfilled by harnessing essential artificial intelligence tools in the water sector.