Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water
This study explored the application of machine learning, specifically artificial neural network (ANN), to create prediction models for manganese (Mn) concentration in soil and surface water (SW) on the island province with two open mine pits overflowing to two major rivers that experienced mining di...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/13/2318 |
| _version_ | 1797590761692200960 |
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| author | Cris Edward F. Monjardin Christopher Power Delia B. Senoro Kevin Lawrence M. De Jesus |
| author_facet | Cris Edward F. Monjardin Christopher Power Delia B. Senoro Kevin Lawrence M. De Jesus |
| author_sort | Cris Edward F. Monjardin |
| collection | DOAJ |
| description | This study explored the application of machine learning, specifically artificial neural network (ANN), to create prediction models for manganese (Mn) concentration in soil and surface water (SW) on the island province with two open mine pits overflowing to two major rivers that experienced mining disasters. The two ANN models were created to predict Mn concentrations in soil and SW from 12 and 14 input parameters for soil and SW, respectively. These input parameters were extracted from extensive field data collected at the site during sampling program in 2019, 2021, 2022, and initially processed with spatial analysis via geographic information system (GIS). All datasets were then divided for model training and validation, using 85% and 15% ratio, respectively. Performance evaluation of each model with mean absolute percentage error (MAPE) and root mean squared error (RMSE) confirmed the accuracy of both models. The soil Mn model achieved MAPE and RMSE values of 2.01% and 23.98, respectively. The SW Mn model was split into two models based on SW Mn values within the 0–1 mg/L range and >1 mg/L range. The SW Mn model for >1 mg/L performed better with MAPE and RMSE of 4.61% and 0.17, respectively. Feature reduction was also conducted to identify how the models will perform if some input parameters were excluded. Result showed sufficient accuracy can still be obtained with the removal of 4–5 input parameters. This study and these models highlight the benefit of ANN to the scientific community and government units, for predicting Mn concentration, of similar environmental conditions. |
| first_indexed | 2024-03-11T01:25:06Z |
| format | Article |
| id | doaj.art-0746b68777f5482cade9bddad643bc02 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T01:25:06Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-0746b68777f5482cade9bddad643bc022023-11-18T17:46:25ZengMDPI AGWater2073-44412023-06-011513231810.3390/w15132318Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface WaterCris Edward F. Monjardin0Christopher Power1Delia B. Senoro2Kevin Lawrence M. De Jesus3School of Civil, Environmental and Geological Engineering, Mapua University, 658 Muralla St., Intramuros, Manila 1002, PhilippinesDepartment of Civil and Environmental Engineering, Western University, 1151 Richmond St., London, ON N6A 5B9, CanadaSchool of Civil, Environmental and Geological Engineering, Mapua University, 658 Muralla St., Intramuros, Manila 1002, PhilippinesResiliency and Sustainable Development Center, Yuchengco Innovation Center, Mapua University, 658 Muralla St., Intramuros, Manila 1002, PhilippinesThis study explored the application of machine learning, specifically artificial neural network (ANN), to create prediction models for manganese (Mn) concentration in soil and surface water (SW) on the island province with two open mine pits overflowing to two major rivers that experienced mining disasters. The two ANN models were created to predict Mn concentrations in soil and SW from 12 and 14 input parameters for soil and SW, respectively. These input parameters were extracted from extensive field data collected at the site during sampling program in 2019, 2021, 2022, and initially processed with spatial analysis via geographic information system (GIS). All datasets were then divided for model training and validation, using 85% and 15% ratio, respectively. Performance evaluation of each model with mean absolute percentage error (MAPE) and root mean squared error (RMSE) confirmed the accuracy of both models. The soil Mn model achieved MAPE and RMSE values of 2.01% and 23.98, respectively. The SW Mn model was split into two models based on SW Mn values within the 0–1 mg/L range and >1 mg/L range. The SW Mn model for >1 mg/L performed better with MAPE and RMSE of 4.61% and 0.17, respectively. Feature reduction was also conducted to identify how the models will perform if some input parameters were excluded. Result showed sufficient accuracy can still be obtained with the removal of 4–5 input parameters. This study and these models highlight the benefit of ANN to the scientific community and government units, for predicting Mn concentration, of similar environmental conditions.https://www.mdpi.com/2073-4441/15/13/2318artificial neural networkheavy metalsspatial analysisprediction modelenvironmental monitoringmachine learning |
| spellingShingle | Cris Edward F. Monjardin Christopher Power Delia B. Senoro Kevin Lawrence M. De Jesus Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water Water artificial neural network heavy metals spatial analysis prediction model environmental monitoring machine learning |
| title | Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water |
| title_full | Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water |
| title_fullStr | Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water |
| title_full_unstemmed | Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water |
| title_short | Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water |
| title_sort | application of machine learning for prediction and monitoring of manganese concentration in soil and surface water |
| topic | artificial neural network heavy metals spatial analysis prediction model environmental monitoring machine learning |
| url | https://www.mdpi.com/2073-4441/15/13/2318 |
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