Rewards, risks and responsible deployment of artificial intelligence in water systems

Richards, Catherine E.; Tzachor, Asaf; Avin, Shahar; Fenner, Richard

doi:10.1038/s44221-023-00069-6

Cited by 21 publications

(7 citation statements)

References 77 publications

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“…Data transmission involves the transfer of collected data to either a central control system or a cloudbased platform through the utilisation of wireless communication technology. This enables the ongoing surveillance and remote retrieval of data pertaining to the quality of water [52]. The data analysis process involves real-time examination of the data in order to identify any anomalies or deviations from pre-established water quality criteria.…”

Section: Fig4 Real Data Monitoring Systemmentioning

confidence: 99%

Flowing Futures: Innovations in WASH for Sustainable Water, Sanitation, and Hygiene

Kumar,

Chaturvedi,

Rao

et al. 2023

E3S Web Conf.

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The future of Water, Sanitation, and Hygiene (WASH) entails a combination of urgent difficulties and unparalleled prospects. In the pursuit of achieving universal access to clean water and sanitation, together with the promotion of sanitary practises, innovation has emerged as a crucial driver for sustainable advancement within the global society. The present study, entitled “Flowing Futures: Innovations in Water, Sanitation, and Hygiene (WASH) for Sustainable Provision of Water, Sanitation, and Hygiene,” examines the ever-changing terrain of WASH by examining novel approaches, with the ultimate goal of visualising a future in which all individuals have equal and fair access to these essential human entitlements. This study focuses on the convergence of WASH (Water, Sanitation, and Hygiene) with technology, emphasising advancements in water purification, sanitation infrastructure, and interventions targeting behaviour change. This study investigates the impact of recent improvements in data analytics, remote sensing, and smart infrastructure on the monitoring and management of water resources. These technological developments have the potential to significantly improve efficiency and enable prompt response during times of crisis. Also, the initiative known as “Flowing Futures” places significant emphasis on the importance of community-led strategies, acknowledging that the establishment of sustainable solutions is contingent upon local empowerment and active participation. This study examines many case studies from different geographical areas, demonstrating the significant effects of participatory programmes that enable communities to assume responsibility for their water, sanitation, and hygiene (WASH) services. These initiatives also promote a sense of stewardship towards the environment.

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Section: Fig4 Real Data Monitoring Systemmentioning

confidence: 99%

Flowing Futures: Innovations in WASH for Sustainable Water, Sanitation, and Hygiene

Kumar,

Chaturvedi,

Rao

et al. 2023

E3S Web Conf.

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“…This can be simply measured quickly and accurately. ACCIONA is another strategic symbol of the active employment of AI and IOT as it is considered a reference sign in treatment and management of water in the 5 continents and through more than 40000 employees (Richards, 2023).…”

Section: How Ai and Iot Can Support Each Othermentioning

confidence: 99%

“…The employment of AI can be viewed as a smart system that can be used heavily in the kingdom in the management of water especially in distribution. This important resource participates positively in satisfying the sustainability goals for clean water (Richards, 2023).…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Intelligence in Water Management Projects in the Kingdom of Saudi Arabia

Alghadeer

2023

AJT

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This paper investigates the potential for utilizing artificial intelligence (AI) to improve water distribution management in Saudi Arabia. With increasing population and water scarcity, there is a need for more efficient water management. The paper reviews successful applications of AI and internet of things (IoT) in water distribution in other countries, in order to identify best practices that could be applied in Saudi Arabia. Challenges such as lack of infrastructure, data, human capabilities, and regulations are discussed. Findings indicate that AI and IoT can reduce water waste, leaks, and costs, but an adequate infrastructure and vision are needed.

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“…Those traditional methods are time-consuming, labor-intensive, inefficient (i.e., low throughput), and costly (Ahmed et al, 2019;Zainurin et al, 2022). Artificial intelligence (AI), especially machine learning (ML), is promising to address the deficiencies in the traditional approaches to access and ensure safe drinking water supply (Richards et al, 2023). The adaptability, feasibility, and predictive power of ML offer significant advantages over other AI technologies (Willard et al, 2022;Zhu et al, 2022), particularly when handling drinking water quality with a dynamic and complex nature.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review

Feng,

Zhang,

Chen

et al. 2024

Preprint

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Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is an alternative, promising technique to assess and ensuring safe drinking water supply. Existing reviews have summarized the applications of ML in safe drinking water supply from different aspects. However, a state-of-the-art, comprehensive review is missing that focuses on applying ML to monitor, simulate, predict, and control drinking water quality, especially in municipal engineered water systems. This review, therefore, critically compiles the applications of ML in assessing and ensuring water quality in engineered water systems. To be comprehensive, we also cover the applications of ML in other drinking-water-related settings such as water sources and water purification processes. We explain the basic mechanics and workflows of ML, focusing on the applications of ML to access and control key factors or etiologies in drinking water from the physical, chemical, and microbiological aspects. Those factors or etiologies affect water quality and public health, such as water pipeline failures, disinfectant by-products, heavy metals, opportunistic pathogens, biofilms, and antimicrobial resistance genes. We then illustrate the distribution of ML models across research topics in safe drinking water supply. Finally, we discuss the challenges and outlooks for the applications of machine learning in safe drinking water supply. This is the first review summarizing the feasibility and applications of ML in assessing and ensuring water quality in municipal engineered water systems as well as other related water environments.

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Rewards, risks and responsible deployment of artificial intelligence in water systems

Cited by 21 publications

References 77 publications

Flowing Futures: Innovations in WASH for Sustainable Water, Sanitation, and Hygiene

Flowing Futures: Innovations in WASH for Sustainable Water, Sanitation, and Hygiene

The Use of Artificial Intelligence in Water Management Projects in the Kingdom of Saudi Arabia

Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review

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