Improving the Forecast Accuracy of PM<sub>2.5</sub> Using SETAR-Tree Method: Case Study in Jakarta, Indonesia

Improving the Forecast Accuracy of PM<sub>2.5</sub> Using SETAR-Tree Method: Case Study in Jakarta, Indonesia

Air pollution in Jakarta, one of the most polluted cities globally, has reached critical levels, with PM<sub>2.5</sub> concentrations exceeding the WHO guidelines and posing significant health risks. Accurate forecasting of PM<sub>2.5</sub> is crucial for effective air qualit...

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Bibliographic Details
Main Authors: Dinda Ayu Safira, Heri Kuswanto, Muhammad Ahsan
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Atmosphere
Subjects:
SETAR-Tree
LSTM
PM<sub>2.5</sub>
air pollution
forecasting
nonlinear time series
Online Access:https://www.mdpi.com/2073-4433/16/1/23
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Summary:Air pollution in Jakarta, one of the most polluted cities globally, has reached critical levels, with PM<sub>2.5</sub> concentrations exceeding the WHO guidelines and posing significant health risks. Accurate forecasting of PM<sub>2.5</sub> is crucial for effective air quality management and public health interventions. PM<sub>2.5</sub> exhibits significant nonlinear fluctuations; thus, this study employed two machine learning approaches: self-exciting threshold autoregressive tree (SETAR-Tree) and long short-term memory (LSTM). The SETAR-Tree model integrates regime-switching capabilities with decision tree principles to capture nonlinear patterns, while LSTM models long-term dependencies in time-series data. The results showed that: (1) SETAR-Tree outperformed LSTM, achieving lower RMSE (0.1691 in-sample, 0.2159 out-sample) and MAPE (2.83% in-sample, 2.98% out-sample) compared to LSTM’s RMSE (0.2038 in-sample, 0.2399 out-sample) and MAPE (3.48% in-sample, 4.05% out-sample); (2) SETAR-Tree demonstrated better responsiveness to sudden regime changes, capturing complex pollution patterns influenced by meteorological and anthropogenic factors; (3) PM<sub>2.5</sub> in Jakarta often exceeds the WHO limits, highlighting this study’s importance in supporting strategic planning and providing an early warning system to reduce outdoor activity during extreme pollution.
ISSN:2073-4433

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