首页|Recent Findings from Florida A&M University Provides New Insights i nto Artificial Intelligence (Artificial Intelligence for Water Consumption Asses sment: State of the Art Review)
Recent Findings from Florida A&M University Provides New Insights i nto Artificial Intelligence (Artificial Intelligence for Water Consumption Asses sment: State of the Art Review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Artificial Intelligence is now available. According to news reporting originating in Tallahassee, Flori da, by NewsRx journalists, research stated, "In recent decades, demand for fresh water resources has increased the risk of severe water stress. With the growing prevalence of artificial intelligence (AI), many researchers have turned to it a s an alternative to linear methods to assess water consumption (WC)."Financial support for this research came from National Institute of Food and Agr iculture. The news reporters obtained a quote from the research from Florida A& M University, "Using the PRISMA (Preferred Reporting Items for Systematic Review s and Meta-Analyses) framework, this study utilized 229 screened publications id entified through database searches and snowball sampling. This study introduces novel aspects of AI's role in water consumption assessment by focusing on innova tion, application sectors, sustainability, and machine learning applications. It also categorizes existing models, such as standalone and hybrid, based on input , output variables, and time horizons. Additionally, it classifies learnable par ameters and performance indexes while discussing AI models' advantages, disadvan tages, and challenges. The study translates this information into a guide for se lecting AI models for WC assessment. As no one-size-fits-all AI model exists, th is study suggests utilizing hybrid AI models as alternatives. These models offer flexibility regarding efficiency, accuracy, interpretability, adaptability, and data requirements. They can address the limitations of individual models, lever age the strengths of different approaches, and provide a better understanding of the relationships between variables."
TallahasseeFloridaUnited StatesNor th and Central AmericaArtificial IntelligenceEmerging TechnologiesMachine LearningRisk and PreventionFlorida A&M University
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