A Hybrid Model for the Prediction of Air Pollutants Concentration, Based on Statistical and Machine Learning Techniques

Minutti, Carlos; Arellano-Vázquez, Magali; Zamora-Machado, Marlene

doi:10.1007/978-3-030-89820-5_21

Cited by 3 publications

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Putri,

Caraka,

Toharudin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Particulate matter forecasting is fundamental for early warning and controlling air pollution, especially PM2.5. The increase in this level of concentration will lead to a negative impact on public health. This study develops a hybrid model of CNN-LSTM and CONV-LSTM by combining a convolutional neural network (CNN) with an LSTM network to forecast PM2.5 concentration for the next few hours in Kemayoran DKI Jakarta, which is known as a busy area. We discovered the advantages of CNN in effectively extracting features and LSTM in learning long-term historical data from PM2.5 concentration time series data. The predictive model of CNN-LSTM is carried out in a different architecture where the CNN process is carried out first to become the input of LSTM. For CONV-LSTM, it is carried out in one architecture where the multiplication in the LSTM architecture is coupled with the convolution process. This research will explain how the method of developing hybrid CNN-LSTM and CONV-LSTM in predicting PM2.5 concentrations. Based on metric evaluation, the two models are compared to find the best model. Both predictive models produce MAPE values that fall into the good enough category with values <20%. Results were obtained for CONV-LSTM with MAE worth 6.52, RMSE 8.55, and MAPE 16.39%. As a result, the CONV-LSTM model performs better than CNN-LSTM in nowcasting PM2.5.

show abstract

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Putri,

Caraka,

Toharudin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Exploring nonlinear effects of air pollution on hospital admissions by disease using gradient boosting machines

Minutti

Galindo

Valdez-Garduno

et al. 2022

2022 19th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

View full text Add to dashboard Cite

Estimating the Impact of Electric Vehicle Adoption on CO₂ Concentrations Using Classification and Time Series Methods

Farhadi,

Palacin,

Blythe

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

A Hybrid Model for the Prediction of Air Pollutants Concentration, Based on Statistical and Machine Learning Techniques

Cited by 3 publications

References 15 publications

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Exploring nonlinear effects of air pollution on hospital admissions by disease using gradient boosting machines

Estimating the Impact of Electric Vehicle Adoption on CO₂ Concentrations Using Classification and Time Series Methods

Contact Info

Product

Resources

About

A Hybrid Model for the Prediction of Air Pollutants Concentration, Based on Statistical and Machine Learning Techniques

Cited by 3 publications

References 15 publications

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM2.5 Level

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM2.5 Level

Exploring nonlinear effects of air pollution on hospital admissions by disease using gradient boosting machines

Estimating the Impact of Electric Vehicle Adoption on CO2 Concentrations Using Classification and Time Series Methods

Contact Info

Product

Resources

About

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Estimating the Impact of Electric Vehicle Adoption on CO₂ Concentrations Using Classification and Time Series Methods