Enhancing Environmental Policy Decisions in Korea and Japan Through AI-Driven Air Pollution Forecast DOI Open Access
Yushin Kim,

J S Kim,

Sunghyun Cho

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10436 - 10436

Published: Nov. 28, 2024

(1) Background: Although numerous artificial intelligence (AI)-based air pollution prediction models have been proposed, research that links key drivers, such as regional industrial facilities, to actionable policy recommendations is required. (2) Methods: This study employs the radial basis function (RBF) and spatial lag features capture interactions among regions, utilizing a transformer model for analysis. The was trained on quality data from South Korea (2010–2022) Japan (2017–2020). (3) Results: achieved mean squared error of 0.045 Korean dataset 0.166 Japanese dataset, outperforming benchmark models, including Support Vector Regression, neural networks, AutoRegressive Integrated Moving Average model. (4) Conclusions: By capturing complex dynamics, proposed provides valuable insights can assist policymakers in developing effective, data-driven strategies reduction at national levels, thereby supporting broader goals sustainability through informed, equitable environmental interventions.

Language: Английский

Optimizing Air Pollution Forecasting Across Temporal Scales: A Case Study in Salamanca, Mexico DOI Creative Commons
Francisco-Javier Moreno-Vazquez, Felipe Trujillo-Romero, Amanda Enrriqueta Violante Gavira

et al.

Earth, Journal Year: 2025, Volume and Issue: 6(1), P. 9 - 9

Published: Feb. 9, 2025

Air pollution forecasting is essential for understanding environmental patterns and mitigating health risks, especially in urban areas. This study investigates the of criterion pollutants—CO,O3,SO2,NO2,PM2.5, PM10—across multiple temporal frames (hourly, daily, weekly, monthly) Salamanca, Mexico, utilizing temporal, meteorological, pollutant data from local monitoring stations. The primary objective to identify robust models capable short- mid-term predictions, despite challenges related inconsistencies missing values. Leveraging low-code PyCaret framework, a benchmark analysis was conducted best-performing each pollutant. Statistical evaluations, including ANOVA Tukey HSD tests, were employed compare model performance across different time frames. results reveal significant variations prediction accuracy depending on both windows, with stronger predictive observed weekly monthly research indicates that incorporation variables enhances forecast highlights value AutoML tools, such as PyCaret, streamlining selection improving overall efficiency.

Language: Английский

Citations

0
Enhancing Environmental Policy Decisions in Korea and Japan Through AI-Driven Air Pollution Forecast DOI Open Access
Yushin Kim,

J S Kim,

Sunghyun Cho

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10436 - 10436

Published: Nov. 28, 2024

(1) Background: Although numerous artificial intelligence (AI)-based air pollution prediction models have been proposed, research that links key drivers, such as regional industrial facilities, to actionable policy recommendations is required. (2) Methods: This study employs the radial basis function (RBF) and spatial lag features capture interactions among regions, utilizing a transformer model for analysis. The was trained on quality data from South Korea (2010–2022) Japan (2017–2020). (3) Results: achieved mean squared error of 0.045 Korean dataset 0.166 Japanese dataset, outperforming benchmark models, including Support Vector Regression, neural networks, AutoRegressive Integrated Moving Average model. (4) Conclusions: By capturing complex dynamics, proposed provides valuable insights can assist policymakers in developing effective, data-driven strategies reduction at national levels, thereby supporting broader goals sustainability through informed, equitable environmental interventions.

Language: Английский

Citations

0