首页|Data on Machine Learning Reported by Researchers at Southern University of Science and Technology (SUSTech) (Application of Machine Learning Models In Groundwater Quality Assessment and Prediction: Progress and Challenges)
Data on Machine Learning Reported by Researchers at Southern University of Science and Technology (SUSTech) (Application of Machine Learning Models In Groundwater Quality Assessment and Prediction: Progress and Challenges)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learning have been published. According to news reporting originating from Shenzhen, People's Republic of China, by NewsRx correspondents, research stated, "Groundwater quality assessment and prediction (GQAP) is vital for protecting groundwater resources. Traditional GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage of being computationally demanding." Financial support for this research came from Ministry of Science and Technology, China. Our news editors obtained a quote from the research from the Southern University of Science and Technology (SUSTech), "Recently, the application of machine learning (ML) in GAQP (GQAPxML) has been widely studied due to ML's reliability and efficiency. While many GQAPxML publications exist, a thorough review is missing. This review provides a comprehensive summary of the development of ML applications in the field of GQAP. First, the workflow of ML modeling is briefly introduced, as are data preparation, model development, model evaluation, and model application. Second, 299 publications related to the topic are filtered, mainly through ML modeling. Subsequently, many aspects of GQAPxML, such as publication trends, the spatial distribution of study areas, the size of data sets, and ML algorithms, are discussed from a bibliometric perspective. In addition, we review in detail the well-established applications and recent findings for several subtopics, including groundwater quality assessment, groundwater quality modeling using groundwater quality parameters, groundwater quality spatial mapping, probability estimation of exceeding the groundwater quality threshold, groundwater quality temporal prediction, and the hybrid use of ML and physics-based models. Finally, the development of GQAPxML is explored from three perspectives: data collection and preprocessing, model building and evaluation, and the broadening of model applications."
ShenzhenPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningSouthern University of Science and Technology (SUSTech)
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