Optimizing urban water sustainability: Integrating deep learning, genetic algorithm, and CMIP6 GCM for groundwater potential zone prediction within a social-ecological-technological framework

Rahman, Mahfuzur; Islam, Md Monirul; Kim, Hyeong-Joo; Alam, Mehtab; Sadiq, Shamsher; Rahman, Md Khalilur; Sadir Hossan, Md; Islam, Md Tariqul; Raju, Matiur Rahman; Alam, Md Shahrior; Ahmad, Syed Ishtiaq; Dewan, Ashraf

doi:10.1016/j.asr.2024.03.033

Advances in Space Research

2024

DOI: 10.1016/j.asr.2024.03.033

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Optimizing urban water sustainability: Integrating deep learning, genetic algorithm, and CMIP6 GCM for groundwater potential zone prediction within a social-ecological-technological framework

Mahfuzur Rahman,

Md Monirul Islam,

Hyeong-Joo Kim

et al.

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Cited by 2 publications

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Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis

Nanda

2024

Advances in Space Research

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Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis

Nanda

2024

Advances in Space Research

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A cross-enterprise collaborative production scheduling decision support algorithm with multi-agent support

Chen,

Yang

2024

Applied Mathematics and Nonlinear Sciences

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This study harnesses the capabilities of intelligent agent technology to develop a framework for cross-enterprise collaborative production scheduling decision-making. It conducts a comprehensive examination of the business processes and production scheduling decisions encapsulated within this framework. The research begins by pinpointing the challenges inherent in cross-enterprise collaborative production scheduling. Subsequently, it introduces a genetic algorithm tailored for agent-based decision-making in this context and delineates its algorithmic parameters. The effectiveness of this approach is validated through a series of simulation experiments focused on a case study of cross-enterprise collaborative production scheduling from an agent-oriented perspective. The findings indicate that implementing the agent structure and genetic algorithms in a scenario involving ten workpieces and ten machines (10×10) results in a new job reach time of 30, a workshop load of 0.5338, and an average reduction in scheduling time of 11.60%. These results underscore the efficacy of the proposed agent structure and genetic algorithms in enhancing decision support for cross-enterprise collaborative production scheduling, thereby laying a scientific foundation for achieving heightened production efficiency through intelligent agent technology.

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Optimizing urban water sustainability: Integrating deep learning, genetic algorithm, and CMIP6 GCM for groundwater potential zone prediction within a social-ecological-technological framework

Cited by 2 publications

References 132 publications

Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis

Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis

A cross-enterprise collaborative production scheduling decision support algorithm with multi-agent support

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