Predicting Pollution Level Using Random Forest: A Case Study of Marilao River in Bulacan Province, Philippines

Jayson M. Victoriano (), Manuel Luis C. Delos Santos (), Albert A. Vinluan () and Jennifer T. Carpio ()
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Jayson M. Victoriano: AMA University, Bulacan State University
Manuel Luis C. Delos Santos: Asian Institute of Computer Studies
Albert A. Vinluan: New Era University
Jennifer T. Carpio: University of the East

International Journal of Computing Sciences Research, 2019, vol. 3, issue 1, 1-12

Abstract: Purpose – This study aims to predict the pollution level that threatens the Marilao River, located in the province of Bulacan, Philippines. The inhabitants of this area are now being exposed to pollution. Contamination of this waterway comes from both formal and informal industries, such as a used lead-acid battery, open dumpsites metal refining, and other toxic metals. Using various water quality parameters like Dissolved Oxygen (DO), Potential of Hydrogen (pH), Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) were the basis for predicting the pollution level. Method – This study used the Data Mining technique based on the sample data collected from January of 2013 to November of 2017. These were used as a training data and test results to predict the river condition with its corresponding pollution level classification indicated with the used of colors such as “Green†for “Normal†, “Yellow†for “Average†, “Orange†for “Polluted†and “Red†for “Highly Polluted†. The model got an accuracy of 91.75% with a Kappa value of 0.8115, interpreted as “Strong†in terms of the level of agreement. Results – The predicted model using the Random Forest have scored 91.75% in terms of correctly classified instances and were able to generate 0.8115 Kappa values which indicate that the model used to produce a strong level of agreement. Conclusion – From 2013 to 2017 based on the data sampling provided by the Environmental Management Bureau (EMB), an attached agency of the Department of Environment and Natural Resources (DENR) in the Philippines mandated to protect and restore the environment, shows that the river is highly polluted. Several issues like, underestimation of the water parameter results have been identified, issues which can be addressed by incorporating more observations to the training process and by validating the resulting model on the different training set. The discretion on decisions about the prediction of the model is attributed to DENR-EMB unit as they have more hands-on experience with regards to monitoring, restoring, protecting the environment.

Keywords: machine learning; river pollution; data mining; random forest (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:jcs:journl:v:3:y:2019:i:1:p:1-12

DOI: 10.25147/ijcsr.2017.001.1.30

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