Mampitiya Lakindu, Rathnayake Namal, Hoshino Yukinobu, Rathnayake Upaka
Water Resources Management and Soft Computing Research Laboratory, Millennium City, Athurugiriya 10150, Sri Lanka.
Department of Civil Engineering, Faculty of Engineering, The University of Tokyo, 1 Chome-1-1 Yayoi, Bunkyo City, Tokyo 113-8656, Japan.
MethodsX. 2024 Jan 5;12:102557. doi: 10.1016/j.mex.2024.102557. eCollection 2024 Jun.
Machine learning techniques have garnered considerable attention in modern technologies due to their promising outcomes across various domains. This paper presents the comprehensive methodology of an optimized and efficient forecasting approach for Particulate Matter 10, specifically tailored to predefined locations. The execution of a comparative analysis involving eight models enables the identification of the most suitable model that aligns with the primary research objective. Notably, the test results underscore the superior performance of an ensemble model, which integrates state-of-the-art methodologies, surpassing the performance of the other seven state-of-the-art models. Adopting a case-specific methodology with machine learning techniques contributes to achieving a notably high regression coefficient (R²≈1) across all models. Furthermore, the study underscores the potential for future endeavors in predicting location-specific environmental factors.•This study focused on forecasting PM10 with machine learning models with the consideration of air quality factors and meteorological factors•Ensemble model was developed for the forecasting purposes with higher performance.
机器学习技术因其在各个领域的 promising outcomes 而在现代技术中备受关注。本文介绍了一种针对细颗粒物10（PM10）的优化高效预测方法的综合方法，该方法专门针对预定义位置量身定制。通过对八个模型进行比较分析，能够识别出与主要研究目标相符的最合适模型。值得注意的是，测试结果强调了集成模型的卓越性能，该模型整合了最先进的方法，超越了其他七个最先进模型的性能。采用针对特定案例的机器学习技术方法有助于在所有模型中实现显著高的回归系数（R²≈1）。此外，该研究强调了未来在预测特定位置环境因素方面进行努力的潜力。•本研究专注于利用考虑空气质量因素和气象因素的机器学习模型预测PM10•为实现更高性能的预测目的开发了集成模型。
