| Water quality estimation using machine learning is a type of data analysis process that uses algorithms to identify patterns in large sets of data related to water quality. This can include identifying pollutants and other potential contamination that could negatively impact quality for drinking purposes, recreational activities or other uses. This helps ensure that the safety of water sources and the quality of recreational activities are constantly monitored and maintained. Thus, in this paper, a set of existing machine learning classifiers is applied to Internet of Things (IoT) sensor data on various water quality parameters, and the results are compared. Subsequently, a meta ensemble classifier that utilizes the soft voting technique of the best four previous classifiers is proposed to enhance estimation accuracy. According to results on the majority of the metrics used, this meta ensemble classifier outperforms all previously considered classifiers. |
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