Eamlm: Enhanced Automated Machine Learning Model for IoT Based Water Quality Analysis with Real-Time Dataset

In the present decade, the quality of water has become the major concern, because of the rapid increase of pollution on water resources. This has been a great threat for all living beings in the planet. Hence, there is always a demand on an efficient model for water quality analysis. With that note, this paper develops an enhanced automated machine learning model called EAMLM. Moreover, the proposed model utilized Internet of Things (IoT) based sensors to obtain the quality factors of water such as pH rate, temperature, nitrogen, phosphorous, total hardness and total dissolved solids (TDS). The model integrates the IoT analysis with the operations of machine learning methods to evaluate the real-time data of water samples obtained from local areas. In particular, the EAMLM algorithm is framed with the combined efficiencies of modified ranking based K-nearest neighbors and random forest (RF) model. Further, Raspberry Pi3 is low cost kit embedded for sample testing and the model is simulated and evaluated using WEKA tool. The classification results show that the EAMLM provided better accuracy than other traditional models.