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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Main Authors: Monje, Jose Claro N, Estuar, Ma. Regina Justina E, Espiritu, Emilyn Q, Enriquez, Erwin P, Oppus, Carlos M, Coronel, Andrei, Guico, Maria Leonora
Format: text
Published: Archīum Ateneo 2016
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Online Access:https://archium.ateneo.edu/ecce-faculty-pubs/29
https://ieeexplore.ieee.org/document/7373128
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Institution: Ateneo De Manila University
id ph-ateneo-arc.ecce-faculty-pubs-1028
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spelling 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
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Sensors
Decision trees
Rivers
Monitoring
Data mining
Predictive models
Cities and towns
Computer Sciences
Databases and Information Systems
Electrical and Computer Engineering
spellingShingle 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
description 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.
format text
author Monje, Jose Claro N
Estuar, Ma. Regina Justina E
Espiritu, Emilyn Q
Enriquez, Erwin P
Oppus, Carlos M
Coronel, Andrei
Guico, Maria Leonora
author_facet Monje, Jose Claro N
Estuar, Ma. Regina Justina E
Espiritu, Emilyn Q
Enriquez, Erwin P
Oppus, Carlos M
Coronel, Andrei
Guico, Maria Leonora
author_sort 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
title_fullStr Towards building a predictive model for remote river quality monitoring for mining sites
title_full_unstemmed Towards building a predictive model for remote river quality monitoring for mining sites
title_sort towards building a predictive model for remote river quality monitoring for mining sites
publisher Archīum Ateneo
publishDate 2016
url https://archium.ateneo.edu/ecce-faculty-pubs/29
https://ieeexplore.ieee.org/document/7373128
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