Academic Journal of Computing & Information Science, 2023, 6(11); doi: 10.25236/AJCIS.2023.061118.
Jianwei Yu1, Ying Zhang2, Minlian Fan1, Guorui Zhao1
1School of Computer Science and Engineering, Guangdong Ocean University Yangjiang Campus, Yangjiang, China
2School of Materials Science and Engineering, Guangdong Ocean University Yangjiang Campus, Yangjiang, China
This study focuses on sustainability issues related to urban light pollution. Utilizing a comprehensive evaluation model, created by integrating ExtraTrees and CatBoost decision tree models along with the ARIMA Time Series model, the study establishes management strategies under varying light pollution levels. Initially, 363 cities were sampled to identify factors affecting light pollution through the analysis of Night Sky Brightness (NSB). Subsequently, four representative regions within Guangzhou were empirically evaluated to affirm the model's robustness and accuracy. Lastly, interventions involving the modification of GDP growth, urbanization, and forest cover indices were simulated to assess their impact on light pollution levels. The findings indicate that targeted interventions can effectively mitigate light pollution.
Light Pollution, ARIMA, Time Series, Intervention Strategies
Jianwei Yu, Ying Zhang, Minlian Fan, Guorui Zhao. Sustainable Management Strategies for Urban Light Pollution Based on Decision Tree and Time Series Modeling. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 144-150. https://doi.org/10.25236/AJCIS.2023.061118.
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