Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation
Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic...
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MDPI AG
2025-04-01
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| author | Yue Hu Yitong Ding Wenjing Jiang |
| author_facet | Yue Hu Yitong Ding Wenjing Jiang |
| author_sort | Yue Hu |
| collection | DOAJ |
| description | Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic regions, this study proposes a novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Air Quality Index (AQI) time-series prediction. Through systematic analysis of multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover diverse climate zones (subtropical to temperate), geographical gradients (coastal to inland), and topographical variations (plains to mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (<i>p</i> < 0.05) revealed non-Gaussian distribution characteristics in the observational data, providing statistical justification for implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation framework extended beyond conventional single-city approaches, demonstrating model generalizability across distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms to replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed model exhibited robust generalization capabilities across geographical divisions (R<sup>2</sup> = 0.92–0.99), establishing a reliable decision-support platform for regionally adaptive air quality early-warning systems. This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications. |
| format | Article |
| id | doaj-art-74fc70e362d84da68eb5cdcb5dbc4a8f |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-74fc70e362d84da68eb5cdcb5dbc4a8f2025-08-20T01:56:20ZengMDPI AGAtmosphere2073-44332025-04-0116551310.3390/atmos16050513Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic DifferentiationYue Hu0Yitong Ding1Wenjing Jiang2School of Sciences, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Sciences, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaFaculty of Economics and Business Administration, Babeş-Bolyai University, 400591 Cluj-Napoca, RomaniaAir pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic regions, this study proposes a novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Air Quality Index (AQI) time-series prediction. Through systematic analysis of multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover diverse climate zones (subtropical to temperate), geographical gradients (coastal to inland), and topographical variations (plains to mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (<i>p</i> < 0.05) revealed non-Gaussian distribution characteristics in the observational data, providing statistical justification for implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation framework extended beyond conventional single-city approaches, demonstrating model generalizability across distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms to replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed model exhibited robust generalization capabilities across geographical divisions (R<sup>2</sup> = 0.92–0.99), establishing a reliable decision-support platform for regionally adaptive air quality early-warning systems. This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications.https://www.mdpi.com/2073-4433/16/5/513air quality index (AQI) predictiondeep learning hybrid model learningGaussian filteringcorrelation analysis |
| spellingShingle | Yue Hu Yitong Ding Wenjing Jiang Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation Atmosphere air quality index (AQI) prediction deep learning hybrid model learning Gaussian filtering correlation analysis |
| title | Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation |
| title_full | Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation |
| title_fullStr | Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation |
| title_full_unstemmed | Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation |
| title_short | Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation |
| title_sort | geographically aware air quality prediction through cnn lstm kan hybrid modeling with climatic and topographic differentiation |
| topic | air quality index (AQI) prediction deep learning hybrid model learning Gaussian filtering correlation analysis |
| url | https://www.mdpi.com/2073-4433/16/5/513 |
| work_keys_str_mv | AT yuehu geographicallyawareairqualitypredictionthroughcnnlstmkanhybridmodelingwithclimaticandtopographicdifferentiation AT yitongding geographicallyawareairqualitypredictionthroughcnnlstmkanhybridmodelingwithclimaticandtopographicdifferentiation AT wenjingjiang geographicallyawareairqualitypredictionthroughcnnlstmkanhybridmodelingwithclimaticandtopographicdifferentiation |