Decoding PM2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM DOI Creative Commons

Minru Chen,

Binglin Liu, Mei Liang

и другие.

Algorithms, Год журнала: 2025, Номер 18(3), С. 167 - 167

Опубликована: Март 14, 2025

With the rapid development of industrialization and urbanization, air pollution is becoming increasingly serious. Accurate prediction PM2.5 concentration great significance to environmental protection public health. Our study takes Nanning urban area, which has unique geographical, climatic source characteristics, as object. Based on dual-time resolution raster data China High-resolution High-quality Dataset (CHAP) from 2012 2023, carried out using SARIMA, Prophet LightGBM models. The systematically compares performance each model spatial temporal dimensions indicators such mean square error (MSE), absolute (MAE) coefficient determination (R2). results show that a strong ability mine complex nonlinear relationships, but its stability poor. obvious advantages in dealing with seasonality trend time series, it lacks adaptability changes. SARIMA based series theory performs well some scenarios, limitations non-stationary heterogeneity. research provides multi-dimensional reference for subsequent predictions, helps researchers select models reasonably according different scenarios needs, new ideas analyzing change patterns, promotes related field science.

Язык: Английский

Decoding PM2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM DOI Creative Commons

Minru Chen,

Binglin Liu, Mei Liang

и другие.

Algorithms, Год журнала: 2025, Номер 18(3), С. 167 - 167

Опубликована: Март 14, 2025

With the rapid development of industrialization and urbanization, air pollution is becoming increasingly serious. Accurate prediction PM2.5 concentration great significance to environmental protection public health. Our study takes Nanning urban area, which has unique geographical, climatic source characteristics, as object. Based on dual-time resolution raster data China High-resolution High-quality Dataset (CHAP) from 2012 2023, carried out using SARIMA, Prophet LightGBM models. The systematically compares performance each model spatial temporal dimensions indicators such mean square error (MSE), absolute (MAE) coefficient determination (R2). results show that a strong ability mine complex nonlinear relationships, but its stability poor. obvious advantages in dealing with seasonality trend time series, it lacks adaptability changes. SARIMA based series theory performs well some scenarios, limitations non-stationary heterogeneity. research provides multi-dimensional reference for subsequent predictions, helps researchers select models reasonably according different scenarios needs, new ideas analyzing change patterns, promotes related field science.

Язык: Английский

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