Smart Water Resource Management Using Artificial Intelligence—A Review

Somayaji, Siva Rama Krishnan; Nallakaruppan, M. K.; Chengoden, Rajeswari; Koppu, Srinivas; Iyapparaja, M.; Sadhasivam, Jayakumar; Sethuraman, Sankaran

doi:10.3390/su142013384

Cited by 64 publications

(26 citation statements)

References 92 publications

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“…AI models along with the Internet of Things (IoT) framework and conventional methods are helpful for the design of smart WWT systems and the reuse of sewage [53]. An AI model is a useful and powerful tool for the modeling, prediction and optimization of the WWT process and has been widely applied in various aspects of WWT, such as the removal of dyes, heavy metals, nutrients, organics, solids, microbial contamination, drugs and pesticides from water [49,[54][55][56].…”

Section: Applications Of Ai Models In Wwtmentioning

confidence: 99%

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Wang,

Cheng,

Liu

et al. 2023

Sustainability

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In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

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Section: Applications Of Ai Models In Wwtmentioning

confidence: 99%

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Wang,

Cheng,

Liu

et al. 2023

Sustainability

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“…"> Internet of things : The connection of physical devices to the internet enables real‐time basin monitoring and supports adaptive management approaches. Machine learning and artificial intelligence (AI): AI techniques, including machine learning, can handle vast data amounts, detect patterns, predict future conditions, and aid in decision‐making (Krishnan et al, 2022). …”

Section: Strategies To Actualize the Visionmentioning

confidence: 99%

“…Machine learning and artificial intelligence (AI): AI techniques, including machine learning, can handle vast data amounts, detect patterns, predict future conditions, and aid in decision‐making (Krishnan et al, 2022).…”

Section: Strategies To Actualize the Visionmentioning

confidence: 99%

Strategies for a resilient, sustainable, and equitable Mississippi River basin

Guo

2023

River

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The Mississippi River and its vast basin play a critical role in supporting ecological, economic, and social systems in the United States. However, managing this extensive watershed has become increasingly challenging due to a range of complex issues. This paper explores the historical developments in the watershed management of the Mississippi River, including navigation, flood control, sediment management, water supply, and pollution control. The current issues affecting the resilience, sustainability, and equity of the river and its basin are analyzed, leading to the formulation of a vision for its future. To realize this vision, various strategies are proposed, including embracing integrated water resources management, leveraging information technologies, synergizing green, grey, and blue infrastructure measures, and preparing for extreme weather events. Additionally, addressing equity issues, recognizing the water–energy–food nexus, exploring the river as a carbon capture reservoir, and investigating geoengineering concepts are also discussed. The paper concludes with essential research needs and provides recommendations to foster a resilient, sustainable, and equitable Mississippi River and its basin.

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“…Chlorophyll, pH, dissolved oxygen, heavy metal concentration, chloride, and lead are all indicators (pollutants) of water quality. Some scientists have tried to predict pollution levels using machine learning by feeding in data on water bodies' locations and elevations [114,115]. , and machine learning algorithms might be used to issues including leak control, flow monitoring, overuse, pollution, and the development of strategies for sustainable water consumption (Figure .11).…”

Section: Utilizing Ai For Efficient Water Resource Managementmentioning

confidence: 99%

Innovative Soil, Water and Plant Management Promoting Sustainable Agriculture and Environments :A Review

Al-juthery,

Alkhlefawi,

Al-Taey

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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There is a lot of pressure on today’s agricultural producers to fulfill the increasing food needs brought on by a growing population and a shrinking supply of land and other resources. In order to meet this challenge, many people are using excessive amounts of fertilizers and other chemicals. The soil health, land quality, and environmental impacts have all suffered as a result of this intense agricultural production that disregards ecological sustainability. So, future attempts to feed the expanding population should strive for higher agricultural productivity within sustainable ecosystems. In this context, creative initiatives are required, since business-as-usual approaches lack the capacity to deal with these issues. Agricultural sustainability is defined, and various soil and crop management strategies that aim to maximize crop yield under environmentally sustainable conditions are discussed. These include, but are not limited to, nutrient management, site-specific nutrient management, fertility management, integrated soil-crop system management, sustainable water management, and water conservation. In addition, nutrient management, fertility management, and integrated soil-crop system management have been shown to improve crop yields. According to this analysis, a sustainable production system may be created by integrating the many initiatives found in SCMS practices with both immediate and long-term preventative actions. Reducing chemicals’ consumption, such as that of fertilizers and pesticides, with improvements in the agricultural input use efficiency might cut greenhouse gases emissions while safeguarding the environment. The future of humanity and Earth depends on the success of sustainable agriculture, which has promise if both rich and developing countries work together to pursue ‘our shared destiny’ in order to increase food production with little impact on the environment.

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Smart Water Resource Management Using Artificial Intelligence—A Review

Cited by 64 publications

References 92 publications

A Review on Applications of Artificial Intelligence in Wastewater Treatment

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Strategies for a resilient, sustainable, and equitable Mississippi River basin

Innovative Soil, Water and Plant Management Promoting Sustainable Agriculture and Environments :A Review

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