Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach
Urbanization is often associated with decreased rainwater quality due to many factors, such as uncontrolled pollution and waste disposal. Therefore, managing water quality impacts in urban areas must be addressed to protect our environment. One of the maintenance steps is installing gross pollu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
INTI International University
2023
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1800/ http://eprints.intimal.edu.my/1800/1/jods2023_10.pdf |
| _version_ | 1848766838429712384 |
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| author | Elpina, Sari Tri Basuki, Kurniawan |
| author_facet | Elpina, Sari Tri Basuki, Kurniawan |
| author_sort | Elpina, Sari |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Urbanization is often associated with decreased rainwater quality due to many factors, such as
uncontrolled pollution and waste disposal. Therefore, managing water quality impacts in urban
areas must be addressed to protect our environment. One of the maintenance steps is installing
gross pollutant traps (GPT). The main objective of GPT is to remove dirty pollutants that are
carried into the rainwater system before the rainwater enters the main river channel. At the same
time, it is essential to understand that tropical climates are always associated with high rainfall
intensity in a short period. It means that the amount of waste that GPT daily captures cannot be
predicted well. It causes other problems, namely the emergence of difficulties in predicting the
amount of waste that must be transported and moved from the GPT location to the final waste
disposal site so that often, the rubbish that the GPT has caught will pile up at the GPT location
without being able to be transported properly. Because the garbage vehicles that must transport
the garbage are insufficient in number and capacity, it is necessary to have a model that can
accurately predict the amount of waste that may be captured by each GPT based on past data on
the amount of garbage that has been captured. This research compares 3 algorithms for predicting
the amount of waste trapped by GPT: Simple Linear Regression, Multiple Linear Regression, and
Polynomial Regression. The results show pretty good accuracy in our model, which is the RMSE
is 1000. Next, a simple application was developed to lead the implementation of a load
optimization scenario to show the importance of predicting the number of rubbish traps by each
GPT by calculating how many trucks should be used to carry the garbage to the final waste disposal
site. |
| first_indexed | 2025-11-14T11:57:30Z |
| format | Article |
| id | intimal-1800 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:57:30Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-18002023-10-05T03:10:52Z http://eprints.intimal.edu.my/1800/ Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach Elpina, Sari Tri Basuki, Kurniawan QA Mathematics QA76 Computer software T Technology (General) TC Hydraulic engineering. Ocean engineering Urbanization is often associated with decreased rainwater quality due to many factors, such as uncontrolled pollution and waste disposal. Therefore, managing water quality impacts in urban areas must be addressed to protect our environment. One of the maintenance steps is installing gross pollutant traps (GPT). The main objective of GPT is to remove dirty pollutants that are carried into the rainwater system before the rainwater enters the main river channel. At the same time, it is essential to understand that tropical climates are always associated with high rainfall intensity in a short period. It means that the amount of waste that GPT daily captures cannot be predicted well. It causes other problems, namely the emergence of difficulties in predicting the amount of waste that must be transported and moved from the GPT location to the final waste disposal site so that often, the rubbish that the GPT has caught will pile up at the GPT location without being able to be transported properly. Because the garbage vehicles that must transport the garbage are insufficient in number and capacity, it is necessary to have a model that can accurately predict the amount of waste that may be captured by each GPT based on past data on the amount of garbage that has been captured. This research compares 3 algorithms for predicting the amount of waste trapped by GPT: Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression. The results show pretty good accuracy in our model, which is the RMSE is 1000. Next, a simple application was developed to lead the implementation of a load optimization scenario to show the importance of predicting the number of rubbish traps by each GPT by calculating how many trucks should be used to carry the garbage to the final waste disposal site. INTI International University 2023-10 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1800/1/jods2023_10.pdf Elpina, Sari and Tri Basuki, Kurniawan (2023) Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach. Journal of Data Science, 2023 (10). pp. 1-14. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA Mathematics QA76 Computer software T Technology (General) TC Hydraulic engineering. Ocean engineering Elpina, Sari Tri Basuki, Kurniawan Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach |
| title | Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach |
| title_full | Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach |
| title_fullStr | Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach |
| title_full_unstemmed | Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach |
| title_short | Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach |
| title_sort | waste prediction in gross pollutant trap using machine learning approach |
| topic | QA Mathematics QA76 Computer software T Technology (General) TC Hydraulic engineering. Ocean engineering |
| url | http://eprints.intimal.edu.my/1800/ http://eprints.intimal.edu.my/1800/ http://eprints.intimal.edu.my/1800/1/jods2023_10.pdf |