Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia

• 主要な著者: Latif S.D., Almalayih M., Yafouz A., Ahmed A.N., Zaini N., Irwan D., AlDahoul N., Sherif M., El-Shafie A.
• その他の著者: 57216081524
• フォーマット: 論文
• 出版事項: Elsevier B.V. 2024
• 主題: Air Quality; Carbon monoxide concentration; Machine learning; Predictive model; Uncertainty analysis; Air quality; Atmospheric humidity; Decision trees; Forecasting; Learning algorithms; Learning systems; Multilayer neural networks; Nitric oxide; Nitrogen oxides; Quality control; Sulfur dioxide; Support vector machines; Wind; Atmospheric carbon monoxide; Machine learning algorithms; Machine learning techniques; Machine-learning; Malaysia; Multilayers perceptrons; Neural-networks; Performance; Predictive models; Carbon monoxide

Insidious toxin carbon monoxide (CO) can imitate a wide range of different disease states. Clinicians have, and will continue to have, serious concerns about the impact of CO imbalances on the human body. Carbon monoxide concentration has been exceeding the allowable levels in Malaysia. Owing to thi...