Towards building a predictive model for remote river quality monitoring for mining sites
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...
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2016
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ph-ateneo-arc.ecce-faculty-pubs-10282022-01-27T03:33:14Z Towards building a predictive model for remote river quality monitoring for mining sites Monje, Jose Claro N Estuar, Ma. Regina Justina E Espiritu, Emilyn Q Enriquez, Erwin P Oppus, Carlos M Coronel, Andrei Guico, Maria Leonora 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. 2016-01-01T08:00:00Z text https://archium.ateneo.edu/ecce-faculty-pubs/29 https://ieeexplore.ieee.org/document/7373128 Electronics, Computer, and Communications Engineering Faculty Publications Archīum Ateneo Sensors Decision trees Rivers Monitoring Data mining Predictive models Cities and towns Computer Sciences Databases and Information Systems Electrical and Computer Engineering |
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Sensors Decision trees Rivers Monitoring Data mining Predictive models Cities and towns Computer Sciences Databases and Information Systems Electrical and Computer Engineering Monje, Jose Claro N Estuar, Ma. Regina Justina E Espiritu, Emilyn Q Enriquez, Erwin P Oppus, Carlos M Coronel, Andrei Guico, Maria Leonora Towards building a predictive model for remote river quality monitoring for mining sites |
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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. |
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Monje, Jose Claro N Estuar, Ma. Regina Justina E Espiritu, Emilyn Q Enriquez, Erwin P Oppus, Carlos M Coronel, Andrei Guico, Maria Leonora |
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Monje, Jose Claro N Estuar, Ma. Regina Justina E Espiritu, Emilyn Q Enriquez, Erwin P Oppus, Carlos M Coronel, Andrei Guico, Maria Leonora |
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Monje, Jose Claro N |
title |
Towards building a predictive model for remote river quality monitoring for mining sites |
title_short |
Towards building a predictive model for remote river quality monitoring for mining sites |
title_full |
Towards building a predictive model for remote river quality monitoring for mining sites |
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Towards building a predictive model for remote river quality monitoring for mining sites |
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Towards building a predictive model for remote river quality monitoring for mining sites |
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towards building a predictive model for remote river quality monitoring for mining sites |
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Archīum Ateneo |
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2016 |
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https://archium.ateneo.edu/ecce-faculty-pubs/29 https://ieeexplore.ieee.org/document/7373128 |
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