Performance of machine learning models to forecast PM10 levels

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 pre...

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Bibliografiske detaljer

Principais autores:	Lakindu Mampitiya, Namal Rathnayake, Yukinobu Hoshino, Upaka Rathnayake
Format:	Artigo
Sprog:	Inglês
Udgivet:	Elsevier 2024-06-01
Serier:	MethodsX
Fag:	Ensemble Model, XGBoost, CatBoost, LightGBM, LSTM, Bi-LSTM, GRU, ANN
Online adgang:	http://www.sciencedirect.com/science/article/pii/S2215016124000128
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