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 |
| Summary: | 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. |
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