Towards building a predictive model for remote river quality monitoring for mining sites

• Main Authors: Estuar, Maria Regina Justina E., Oppus, Carlos, Espiritu, Emilyn Q., Coronel, Andrei D., Enriquez, Erwin, Guico, Maria Leonora, Claro Monje, Jose
• Format: Conference Proceeding
• Language: English
• Subjects: Cities and towns; Data mining; Decision trees; Electronics; low cost mobile electronic sensors; machine learning; Mathematical models; Mercury (metal); Mining; Monitoring; Predictive models; quality monitoring; Rivers; Salinity; Sensors; Water quality
• ISSN: 2159-3442; 2159-3450
• DOI: 10.1109/TENCON.2015.7373128

Most, if not all, mining sites in the Philippines are not equipped with expensive or modern monitoring tools to check for quality of soil, water and air elements which are relevant to ensure safety and wellness of miners. This study focused on the development of low cost mobile electronic sensors to monitor quality of water from rivers near mining sites. Low cost electronic sensors connected to a smart phone were developed to capture dissolved oxygen (DO2), pH, Turbidity, Temperature, and Salinity. The data for mercury (Hg) and arsenic (As) were obtained through AAS analyses to form baseline data for the model. Data was collected for over a period of one year, with site visits once every two months. A conditional inference tree (ctree) using recursive binary partitioning was used to generate the prediction model using 70 - 30 split on the training and test data set. The multi-feature model returns Good, Not Good or Unknown based on the scores of each element. The results showed a possible three feature model with significant results for site, salinity and pH balance.