所蔵: Forecasting of fine particulate matter based on LSTM and optimization algorithm

Forecasting of fine particulate matter based on LSTM and optimization algorithm

Accurate air pollution forecasting may provide valuable information for urban planning to maintain environmental sustainability and reduce mortality risk due to health problems. The city with higher industrial activities, traffic congestion, population density, and energy consumption is most likely...

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書誌詳細
主要な著者:	Zaini N., Ahmed A.N., Ean L.W., Chow M.F., Malek M.A.
その他の著者:	56905328500
フォーマット:	論文
出版事項:	Elsevier Ltd 2024
主題:	Air pollution Artificial intelligence Deep learning Optimization Particulate matter Air quality Empirical mode decomposition Energy utilization Forecasting Health risks Learning algorithms Long short-term memory Particles (particulate matter) Population statistics Sustainable development Traffic congestion Air pollution forecasting Air quality monitoring stations Fine particulate matter Forecasting accuracy Optimisations Optimization algorithms Particulate Matter Performance PM 2.5 Particle swarm optimization (PSO)
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Forecasting of fine particulate matter based on LSTM and optimization algorithm

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