首页|Researcher from Universidad Tecnologica de Bolivar Publishes New Studies and Fin dings in the Area of Machine Learning (Assessing the impact of missing data on w ater quality index estimation: a machine learning approach)
Researcher from Universidad Tecnologica de Bolivar Publishes New Studies and Fin dings in the Area of Machine Learning (Assessing the impact of missing data on w ater quality index estimation: a machine learning approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news originating from the Universida d Tecnologica de Bolivar by NewsRx correspondents, research stated, "Despite the regulations and controls implemented worldwide by governments and institutions to ensure the availability and quality of water resources, many water sources re main susceptible to contamination. This contamination poses significant risks to human health and can lead to substantial economic losses." The news correspondents obtained a quote from the research from Universidad Tecn ologica de Bolivar: "One of the challenges in this context is the presence of mi ssing or incomplete data, which can arise from various factors such as the metho dology used or the expertise of personnel involved in sample collection and anal ysis. The existence of such data gaps hampers the accurate analysis that can be conducted. To address this issue and estimate a water quality index from the ava ilable samples, it is crucial to handle missing information appropriately to avo id biased calculations. This study focuses on the application of machine learnin g methods for imputing missing data in water samples. Furthermore, it quantifies the performance of different models based on the distribution of the obtained d ata. By applying 10 distinct methods to a sample of water quality data, the most effective approaches, namely Bayesian Ridge, Gradient Boosting, Ridge, Support Vector Machine, and Theil-Sen regressors, were identified. The selection of thes e models was based on the evaluation of two estimation error metrics: average pe rcent bias (PBIAS) and Kling-Gupta Efficiency statistic (KGEss)."
Universidad Tecnologica de BolivarCybo rgsEmerging TechnologiesMachine Learning
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