A Review of Artificial Intelligence Applications to Achieve Water-related Sustainable Development Goals

This paper reviews the Artificial Intelligence (AI) applications that help achieve water-related Sustainable Development Goals (SDGs). Current applications of AI in the water sector include i) predictive maintenance of water infrastructure, ii) forecasting water demand and consumption, iii) monitoring water reservoirs and dams, iv) tracking water quality, and v) monitoring and predicting water-related disasters. These applications contribute to achieving water-related SDG targets, specifically 3, 6, 11, and 15. The literature review shows that: i) the rate of adoption of AI-based solutions in predictive maintenance of water infrastructure has accelerated, as AI becomes increasingly accessible, and data analytics and smart sensors become more efficient and affordable; ii) deep learning technology has enabled a new generation of water management systems, which can generate short-term (daily) and long-term (annual) forecasts. iii) as Asia and South America experience an increase in water reservoir and dam construction, AI-based techniques are being successfully implemented in reservoir development and operation; iv) water quality monitoring has been the most significantly impacted by AI relative to other applications, as AI is used to examine small samples and large water bodies, and for real time water quality monitoring; v) AI can be used to forecast water-related disasters with higher accuracy, frequency and lead time, allowing for focused management of post-disaster activity. The paper ends by highlighting the challenges of adopting AI to achieve water-related SDGs.