Prediction of Waste Volume Based on Annual Trends using Data Mining
Keywords:
data mining, Waste Management, XGBoost, Recycling, Waste Volume PredictionAbstract
Waste management has become a critical issue in many large cities due to the increasing volume of waste generated. This study aims to predict waste volume based on annual trends using data mining techniques with the XGBoost model. The data analyzed includes total waste from 2003 to 2017, focusing on plastic, construction, and food waste types. The prediction results show that the XGBoost model can follow seasonal fluctuations in the data, although there are discrepancies in certain periods, such as approaching 2040. Overall, the waste volume trend shows a significant increase, especially in plastic and construction waste, which aligns with the growth in plastic consumption and infrastructure development. Although there has been an increase in the amount of waste recycled, its proportion to the total waste remains low, indicating that recycling programs are not yet optimal. Based on this study, more effective policies and strengthened recycling infrastructure are needed to improve the proportion of recycled waste, as well as data-driven waste management planning to support waste reduction and more sustainable management in the future
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