Identifying AQI Drivers Through Ensemble Learning and LULC-Based Analysis Across the COVID-19 Transition in Haryana, India (2019–2024)

Identifying AQI Drivers Through Ensemble Learning and LULC-Based Analysis Across the COVID-19 Transition in Haryana, India (2019–2024) | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 October 2025 V1 Latest version Share on Identifying AQI Drivers Through Ensemble Learning and LULC-Based Analysis Across the COVID-19 Transition in Haryana, India (2019–2024) Authors : Ajay Dheekwal 0009-0006-0042-3419 [email protected] , Kanwarpreet Singh 0000-0002-7012-1815 , Abhishek Sharma , and Sahil Sharma Authors Info & Affiliations https://doi.org/10.22541/au.175975803.33862670/v1 131 views 71 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Atmospheric pollution significantly threatens environmental sustainability and public health, particularly in rapidly developing urban areas like Haryana, India. The research introduces a holistic framework for predicting the Air Quality Index (AQI) by combining Inverse Distance Weighting (IDW) spatial interpolation with a stacked ensemble machine learning technique. Daily average levels of critical pollutants—PM 2 . 5 , PM 10 , NO 2 , SO 2 , CO, O 3 , and NH 3 —were gathered from 2019 to 2024 at 24 CPCB monitoring stations, accumulating over 43,800 data points and LULC from Bhuvan. Statistical methods, including box plots, PCA, correlation heat maps, and VIF analysis, verified PM 2 . 5 and PM 10 as the primary contributors to AQI, exhibiting a strong correlation (r ≈ 0.87) and seasonal fluctuations. The ensemble model, which integrates RF, SVM, ANN, GPR, and XGBoost within a linear regression meta-learner (developed in MATLAB), showcased exceptional performance with high R 2 and low RMSE values, surpassing the performance of individual models. Feature importance and SHAP analysis confirmed the significant impact of particulate matter. The proposed framework enhances predictive accuracy and improves interpretability, providing actionable insights for environmental organizations. While individual machine learning models and SHAP have been used in prior AQI studies, this study’s novelty lies in their integration within a spatial forecasting pipeline combining IDW-based interpolation, stacked ensemble learning, and explainability techniques—offering spatial completeness, interpretability, and model robustness in a unified framework. Future advancements could further integrate meteorological factors and satellite-derived data to improve spatial-temporal adaptability. Supplementary Material File (figure.docx) Download 2.13 MB File (manuscript.docx) Download 123.01 KB File (tables.docx) Download 16.07 KB Information & Authors Information Version history V1 Version 1 06 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords - machine learning air quality index (aqi) extreme xgboost random forest (rf) stacked model Authors Affiliations Ajay Dheekwal 0009-0006-0042-3419 [email protected] Chandigarh University Institute of Engineering View all articles by this author Kanwarpreet Singh 0000-0002-7012-1815 Chandigarh University University Centre for Research & Development View all articles by this author Abhishek Sharma Chandigarh University University Centre for Research & Development View all articles by this author Sahil Sharma Chandigarh University University Centre for Research & Development View all articles by this author Metrics & Citations Metrics Article Usage 131 views 71 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ajay Dheekwal, Kanwarpreet Singh, Abhishek Sharma, et al. Identifying AQI Drivers Through Ensemble Learning and LULC-Based Analysis Across the COVID-19 Transition in Haryana, India (2019–2024). Authorea . 06 October 2025. DOI: https://doi.org/10.22541/au.175975803.33862670/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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