Prediction of AQI for Urban Metropolis Using MLSTM-GRU Model

Author:

Sarkar Nairita,Keserwani Pankaj Kumar,Govil Mahesh Chandra

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Balaraman, S., Partheeban, P., Elamparithi, P.N., Manimozhi, S.: Application of LSTM models in predicting particulate matter ($$\text{pm}_{2. 5}$$) levels for urban area. J. Eng. Res. 10(3B) (2022)

2. Begum, A., Fatima, F., Sabahath, A.: Implementation of deep learning algorithm with perceptron using TenzorFlow library. In: 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0172–0175. IEEE (2019)

3. Brassington, G.: Mean absolute error and root mean square error: which is the better metric for assessing model performance? In: EGU General Assembly Conference Abstracts, p. 3574 (2017)

4. Gilik, A., Ogrenci, A.S., Ozmen, A.: Air quality prediction using CNN+LSTM-based hybrid deep learning architecture. Environ. Sci. Pollut. Res. 1–19 (2022)

5. Hu, J., et al.: An optimized hybrid deep learning model for PM2.5 and O3 concentration prediction. Air Qual. Atmos. Health 16(4), 857–871 (2023)

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