Abstract—This study aims to predict the pollution level that threatens the Marilao-Meycauayan-Obando River System (MMORS), 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), Total Suspended Solids (TSS), Nitrate, Phosphate, and Coliform are the basis for predicting the pollution level. Base on the sample data collected from January 2013 to May 2018. These are used as a training data and test results to predict the river condition with its corresponding pollution level classification indicated. Random Forest decision tree model got an accuracy of 99.38% with a Kappa value of 0.8303 interpreted as “Strong” in terms of the level of agreement and GIS model shows the heat map of the different water quality parameter and Water Quality Index (WQI) spatial distribution, the majority of the sampling station are greatly
Index Terms—Machine learning, river pollution, random forest and GIS.
J. M. Victoriano is with Bulacan State University, City of Malolos, Bulacan, Philippines. He is also with AMA University Quezon City, Philippines (e-mail: firstname.lastname@example.org).
L. L. Lacatan is with the College of Engineering at AMA, University Quezon City, Philippines (e-mail: email@example.com).
A. A. Vinluan is with AMA University Quezon, City Philippines (e-mail: firstname.lastname@example.org).
Cite: Jayson M. Victoriano, Luisito L. Lacatan, and Albert A. Vinluan, "Predicting River Pollution Using Random Forest Decision Tree with GIS Model: A Case Study of MMORS, Philippines," International Journal of Environmental Science and Development vol. 11, no. 1, pp. 36-42, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).