The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

Kamyab, Hesam; Khademi, Tayebeh; Chelliapan, Shreeshivadasan; SaberiKamarposhti, Morteza; Rezania, Shahabaldin; Yusuf, Mohammad; Farajnezhad, Mohammad; Abbas, Mohamed; Hun Jeon, Byong; Ahn, Yongtae

doi:10.1016/j.rineng.2023.101566

Cited by 87 publications

(7 citation statements)

References 73 publications

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“…By ensuring that crops receive optimal moisture levels at all times, IoT-based irrigation systems can improve plant growth, reduce stress, and increase the quantity and quality of the harvest. This precision supports sustainable agricultural practices and boosts profitability for farmers (Adelekan et al, 2024;Kamyab et al, 2023).…”

Section: Iot-based Irrigation Systems In Agriculturementioning

confidence: 92%

The role of theoretical models in IoT-based irrigation systems: A Comparative Study of African and U.S. Agricultural Strategies for Water Scarcity Management

Wisdom Samuel Udo,

Nneka Adaobi Ochuba,

Olatunji Akinrinola

et al. 2024

Int. J. Sci. Res. Arch.

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This review paper presents a comparative study of the theoretical models underpinning the Internet of Things (IoT)-based irrigation systems, focusing on their application in managing water scarcity within agricultural sectors in Africa and the United States. By examining the adaptation and implementation of these models, the paper sheds light on the diverse strategies employed to optimize water use and enhance crop yields in response to the pressing challenge of water scarcity. In Africa, the emphasis is on low-cost, innovative solutions tailored to local conditions and resource constraints, whereas in the U.S., the focus shifts to leveraging advanced technology, scalability, and economic viability. The study underscores the significance of IoT-based irrigation systems in promoting sustainable agriculture and water management practices across different environmental and socio-economic contexts. Through a detailed discussion, the paper reflects on the theoretical and practical implications of these findings for policymakers, farmers, and technology developers, acknowledging the limitations of the current analysis while suggesting directions for future research. The comparative insights highlight the potential of IoT technologies to revolutionize water scarcity management strategies globally, advocating for increased collaboration and innovation in the development of sustainable irrigation solutions.

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Section: Iot-based Irrigation Systems In Agriculturementioning

confidence: 92%

The role of theoretical models in IoT-based irrigation systems: A Comparative Study of African and U.S. Agricultural Strategies for Water Scarcity Management

Wisdom Samuel Udo,

Nneka Adaobi Ochuba,

Olatunji Akinrinola

et al. 2024

Int. J. Sci. Res. Arch.

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“…Machine learning algorithms, a subset of AI, introduced a data-driven approach that surpassed the limitations of traditional rule-based systems. These algorithms could analyze vast datasets, identify patterns, and make predictions with a level of accuracy and efficiency previously unattainable (Kamyab et al, 2023). The use of AI in credit scoring allowed for a more nuanced evaluation of credit risk, considering a broader spectrum of factors beyond the conventional ones.…”

Section: Historical Evolution Of Credit Scoringmentioning

confidence: 99%

AI in credit scoring: A comprehensive review of models and predictive analytics

Wilhelmina Afua Addy,

Adeola Olusola Ajayi-Nifise,

Binaebi Gloria Bello

et al. 2024

Global J. Eng. Technol. Adv.

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This review provides a succinct overview of the comprehensive review exploring the integration of Artificial Intelligence (AI) in credit scoring. The analysis delves into diverse AI models and predictive analytics shaping the contemporary landscape of credit assessment. The review begins by examining the historical context of credit scoring and progresses through the transformative impact of AI on traditional credit assessment methodologies. It scrutinizes various AI models employed in credit scoring, ranging from machine learning algorithms to advanced predictive analytics. Emphasis is placed on elucidating the strengths and limitations of each model, considering factors such as interpretability, accuracy, and scalability. The evolution of credit scoring is discussed, emphasizing the transition from rule-based systems to sophisticated AI-driven approaches. The integration of alternative data sources, such as social media and unconventional financial indicators, is explored, showcasing the expanding scope of AI in capturing a more holistic view of an individual's creditworthiness. The Review underscores the significance of predictive analytics in credit scoring, outlining the nuanced techniques used to forecast credit risk. It elucidates the role of explainable AI, addressing the need for transparency in complex credit scoring models, especially in the context of regulatory compliance and consumer trust. Furthermore, the review highlights the real-world implications of AI in credit scoring, discussing its impact on financial inclusion, risk management, and decision-making processes. The ethical considerations and potential biases associated with AI models are explored, shedding light on the importance of fairness and responsible AI practices in the credit industry. In conclusion, this comprehensive review navigates the intricate landscape of AI in credit scoring, offering a holistic understanding of the models and predictive analytics that underpin modern credit assessment. The synthesis of historical perspectives, model intricacies, and real-world implications makes this review an essential resource for practitioners, researchers, and policymakers in the ever-evolving domain of AI-driven credit evaluation.

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“…Greywater recycling systems further enhance efficiency by treating and reusing non-potable water from activities like laundry for landscape irrigation (Memon et al, 2007;Zadeh, et al, 2013;Leong et al, 2017;Birks, et al, 2003). The integration of artificial intelligence (AI) and data analytics in water management systems allows for predictive modeling, optimizing distribution networks and identifying potential leaks before they escalate (Kamyab et al, 2023;Jenny et al, 2020;Krishnan, et al, 2022). These technological innovations collectively form a robust foundation for enhancing water efficiency in urban areas.…”

Section: Sustainable Water Practices In Urban Areasmentioning

confidence: 99%

Urban Water Management: A Review of Sustainable Practices in the Usa

Zamathula Queen Sikhakhane Nwokediegwu,

Ejike David Ugwuanyi,

Michael Ayorinde Dada

et al. 2024

Eng. sci. technol. j.

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This comprehensive review explores the landscape of Urban Water Management in the United States, focusing on sustainable practices aimed at addressing the challenges posed by rapid urbanization and climate change. With urban areas facing increasing water stress, this study aims to identify, analyze, and evaluate a range of sustainable practices implemented across the country. The review encompasses diverse aspects of Urban Water Management, including water efficiency measures, green infrastructure initiatives, climate change resilience strategies, and pollution mitigation efforts. In examining water efficiency measures, the study investigates technological innovations and policy frameworks that have contributed to optimizing water use in urban settings. Additionally, the role of green infrastructure is explored, emphasizing its benefits and applications through case studies of successful implementations, shedding light on how nature-based solutions can enhance water sustainability. The review delves into the critical dimension of climate change resilience in urban water systems, analyzing the impacts of climate change on water resources and exploring adaptation and mitigation strategies. Infrastructure improvements and integrated planning approaches are examined as essential components in building resilient urban water systems. Addressing pollution mitigation, the study focuses on stormwater management and wastewater treatment. Best management practices and regulatory measures are scrutinized to understand how urban areas are effectively managing and treating water to mitigate pollution and protect water quality. Furthermore, the review highlights the significance of integrated water resources management as a holistic approach to addressing water challenges in urban contexts. Stakeholder engagement and cross-sectoral coordination are emphasized as integral elements in implementing sustainable and comprehensive water management strategies. Through case studies of successful urban water management projects, the review extracts valuable lessons and insights for future implementations. The challenges and opportunities in the current landscape are explored, providing a nuanced understanding of the barriers to sustainable practices and identifying emerging opportunities. This review synthesizes key findings, implications, and recommendations for advancing sustainable urban water management practices in the United States. The insights generated contribute to the ongoing dialogue on effective water management strategies in the face of evolving urban and environmental dynamics. Keywords: Urban Water Management, Sustainable Practices, Water Efficiency, Climate Change, Resilience, United States, Pollution Mitigation, Water Infrastructure.

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The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

Cited by 87 publications

References 73 publications

The role of theoretical models in IoT-based irrigation systems: A Comparative Study of African and U.S. Agricultural Strategies for Water Scarcity Management

The role of theoretical models in IoT-based irrigation systems: A Comparative Study of African and U.S. Agricultural Strategies for Water Scarcity Management

AI in credit scoring: A comprehensive review of models and predictive analytics

Urban Water Management: A Review of Sustainable Practices in the Usa

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