The advent of machine learning and its significant impact on every sector of society requires our attention towards its progressive application in achieving the Sustainable Development Goals (SDGs). Machine learning is a kind of artificial intelligence allowing the machine itself to change its algorithms in order to provide the optimized solution to the subject. In other words, AI is a machine simulating human intelligence, and ML is a subset of artificial intelligence. The Sustainable Development Goals (SDGs) are an all-inclusive set of objectives intended to provide countries with a trail towards peace and prosperity. There are 17 SDGs that are further divided into 169 targets and 304 indicators dealing with everything from ending hunger and protecting marine wildlife to making cities sustainable and reducing gender inequalities. ML is an important tool in meeting these objectives. This chapter explores the application of ML models in achieving these SDGs in an effective manner.
Top1. Introduction
Sustainable Development Goals (SDGs) include 17 goals that were adopted by all United Nations member states in 2015 with interlaced objectives to protect the planet and improve life quality at the global level in a 15-year plan. SDGs are a universal call to the nations to take necessary actions and join hand-in-hand with strategies to improve international partnerships and ensure the protection of the planet. SDGs comprise of 169 targets. To achieve the goals by 2023, there is a need for the mobilization of all stakeholders for the plan and implementation of the appropriate strategy for concrete realization. Progress tracking towards every indicator/target will further help to achieve the goals as per set targets by 2030. To track the progress, data about each indicator is required. Traditionally, statistics from population surveys, individual registrations, and censuses have been used to track progress towards the SDGs. Although many nations wait decades before taking ground measurements for the leading SDG indicators, such data gathering is expensive and requires significant statistical capabilities. Only about half of the SDG indicators have consistent data from more than half of the nations in the world (Danso and Otoo, 2022; Germann and Langergraber, 2022). These data shortages significantly hamper the international community's ability to monitor progress towards the SDGs. Artificial intelligence may be helpful for accomplishment of SDGs (Vinuesa et., 2020; Jean at al., 2016; Seo et al., 2015). AI may help in image recognition, decision-making, prediction, automatic knowledge extraction, pattern recognition from data, interactive communication, and logical reasoning etc. Machine learning (ML) advancements have demonstrated how scarce ground data may be paired with numerous, inexpensive, and regularly updated sources of innovative sensor data to evaluate various SDG-related outcomes, offering hope for filling these data gaps (Yeh et al., 2021). Machine learning (ML) is an area of Artificial Intelligence (AI) that tries to give robots the capacity to derive knowledge from data without explicit programming. To learn how to make judgments based on unobserved information, ML relies heavily on the research and development of algorithms to create models between inputs and outputs. The use of ML in several sub-domains, such as data mining, image recognition, deep learning, and statistical learning, is growing day by day due to its numerous benefits (Goralski and Tan, 2020; Dogo et al., 2019; Ferreira et al., 2020). This paper includes the assessment of the application of AI in 3 selected SDGs (6, 7 & 14).
Figure 1. Flow diagram of role of artificial intelligence and machine learning in achieving SDG 6, 7. and 14
Top2. Sdg 6: Clean Water And Sanitation: Literature Review
One of the most important goals of the 17 Sustainable Development Goals (SDGs) is clean water and sanitation for all, aiming to ensure everyone has access to safe & clean water with proper sanitation (Tortajada and Biswas, 2018). There are 8 targets with 11 indicators associated with SDG 6 to be achieved by 2030 (Werkneh and Gebru, 2022; Garcia et al., 2023). Demand for clean and safe water is increasing daily due to urbanization, population, agricultural needs, and industrial and energy sectors. The need for more fresh & clean water and proper sanitation is due to insufficient management and exploitation of groundwater. Advancements in technology and awareness of society would be better ways to provide universal access to clean water, sanitation, and hygiene water by 2030 (Sherif et al., 2023). Achieving these goals would save around one million yearly deaths from diseases directly linked to contaminated water, inadequate sanitation, and poor hygiene habits.