International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Climate Classification Analysis for Chinese Provinces Using Machine Learning Models |
|---|---|
| ABSTRACT | This study develops a sophisticated machine learning pipeline to classify climate characteristics across 34 Chinese provinces, leveraging the high-resolution ERA5 reanalysis dataset. The methodology integrates advanced feature engineering, principal component analysis (PCA), clustering, and classification to delineate and predict regional climate patterns. Nineteen enhanced features are derived from meteorological variables, encompassing temperature statistics, humidity variability, pressure dynamics, wind characteristics, and specialized indices such as aridity and temperature-humidity. Robust scaling and PCA are employed to reduce dimensionality while retaining approximately 95% of variance, followed by K-means and hierarchical clustering to group provinces into distinct climate classes. Classification models, including K-nearest neighbors (KNN), random forest, and gradient boosting, are rigorously evaluated for predictive performance. Results demonstrate that K-means clustering with seven clusters achieves the highest silhouette score of 0.350, while gradient boosting yields the highest mean cross-validation accuracy of 0.771 (KNN 0.762, random forest 0.738). Thirteen visualizations, including correlation heatmaps, PCA scatter plots, dendrograms, and radar charts, provide deep insights into feature interactions and climate class profiles. This framework offers a scalable, data-driven solution for regional climate analysis, with significant implications for environmental planning, agricultural optimization, and policy formulation in China's diverse climatic landscape. |
| TITLE | |
| AUTHOR | MALOBA ABRAHAM, NAKALONGE HENRY DANIEL School of Artificial Intelligence; School of Computer Science and Technology, Nanjing University of Information Science and Technology, Nanjing, China |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406002 |
| pdf/2_Climate Classification Analysis for Chinese Provinces Using Machine Learning Models.pdf | |
| KEYWORDS | |
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