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Shah Alam is in Selangor, Malaysia and symbolized with several lakes that are popular among local communities and visitors for leisure and recreational activities. Among of these lakes, few lakes need management to look after its hygienic and well-being to serve its purpose as the recreational lake for the nearby community, such as Lake 7/1F, Shah Alam, Selangor. The secluded location of this water reservoir has made it unpopular, and improper management of it could invite an imbalance environment to its surrounding. Therefore, to initiate the effort, an Internet-of-Thing (IoT) water monitoring system is deployed at this lake to study the dissolved oxygen (DO) level in the water that could indicate the health status of this lake. The system consists of a DO sensor, embedded controller, self-charging power supply, wireless data transmission, and IoT dashboard for in-situ DO measurement. From the analysis, the lake has the normal cycle of DO production, but a dangerous condition is detected when the concentrated DO in the lake water is mostly below the saturation value. [ABSTRACT FROM AUTHOR]

Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R 2 ) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario. [Display omitted] • Satellite and site hydrometeorological data are used for water quality prediction. • River dissolved oxygen (DO) was predicted within tropical environment case study. • Hybrid tree-based artificial intelligence models are developed for the DO prediction. • Several feature selection approaches are integrated to optimize the input parameters. • The proposed methodology is provided a robust technology for DO prediction. [ABSTRACT FROM AUTHOR]