A Novel GRU Based Encoder-Decoder Model (GRUED) Using Inverse Distance Weighted Interpolation for Air Quality Forecasting

A Novel GRU Based Encoder-Decoder Model (GRUED) Using Inverse Distance Weighted Interpolation for Air Quality Forecasting

Автор: Tanya Garg, Daljeet Singh Bawa, Sumayya Khalid

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 6 vol.15, 2023 года.

Бесплатный доступ

The alarming environmental concern of air pollution has a severe global impact. Accurate forecasting can help minimize its hazardous implications well in time. Air Quality forecasting is a complex problem in the domain of time series data forecasting. In this paper we propose a novel customized air quality forecaster developed using Gated Recurrent Unit network-based Encoder-Decoder model (GRUED) of Deep Learning using Inverse Distance Weighted Interpolation for forecasting air pollutant concentrations of Delhi, India. The unique composition and customization of our air quality forecaster is a more efficient and better state of the art model for pollutant concentration prediction than its counterparts. Experimental results are indicative that the proposed model outperforms the conventional Deep Learning models. The proposed model was made to forecast air pollutant concentrations of SO2, CO, NO2 and O3. Each pollutant forecast was evaluated by computing MAE and RMSE metrices. MAE values for SO2, CO, NO2 and O3 forecasts were 60.63%, 26.83%, 33.2% and 31.33% lesser for our GRUED model as compared to conventional LSTM model. RMSE values for SO2, CO, NO2 and O3 forecasts were 43.4%, 19.5%, 26.4% and 27.7% lesser for our GRUED model in comparison to LSTM model. The effectiveness and optimal performance of the suggested approach has been established experimentally.

Еще

Encoder-decoder model, sequence-to-sequence model, gated recurrent units neural networks, deep learning, air quality forecasting, timeseries forecasting

Короткий адрес: https://sciup.org/15018844

IDR: 15018844   |   DOI: 10.5815/ijigsp.2023.06.02

Список литературы A Novel GRU Based Encoder-Decoder Model (GRUED) Using Inverse Distance Weighted Interpolation for Air Quality Forecasting

  • Gary. Adamkiewicz and World Health Organization. Regional Office for Europe, WHO guidelines for indoor air quality : selected pollutants.
  • “As many as 22 of world’s 30 most polluted cities are in India: Report | Business Standard News.” https://www.business-standard.com/article/current-affairs/as-many-as-22-of-world-s-30-most-polluted-cities-are-in-india-report-121031600749_1.html (accessed Mar. 08, 2022).
  • “Delhi Air Quality Index (AQI) and India Air Pollution | AirVisual.” https://www.iqair.com/us/india/delhi (accessed Mar. 07, 2022).
  • U. Kumar, K. D. R.-A. Environment, and undefined 2010, “GARCH modelling in association with FFT–ARIMA to forecast ozone episodes,” Elsevier, Accessed: Mar. 07, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1352231010005340
  • D. Domańska and M. Wojtylak, “Application of fuzzy time series models for forecasting pollution concentrations,” Expert Syst Appl, vol. 39, no. 9, pp. 7673–7679, Jul. 2012, doi: 10.1016/J.ESWA.2012.01.023.
  • S. Kumar, S. Mishra, and S. K. Singh, “A machine learning-based model to estimate PM2.5 concentration levels in Delhi’s atmosphere,” Heliyon, vol. 6, no. 11, p. e05618, Nov. 2020, doi: 10.1016/J.HELIYON.2020.E05618.
  • Z. Wang, L. Chen, Z. Ding, and H. Chen, “An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors,” Atmos Environ, vol. 223, p. 117200, Feb. 2020, doi: 10.1016/J.ATMOSENV.2019.117200.
  • D. Wang, S. Wei, H. Luo, C. Yue, O. G.-S. of the Total, and undefined 2017, “A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine,” Elsevier, Accessed: Mar. 07, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0048969716327036
  • X. Xi et al., “A comprehensive evaluation of air pollution prediction improvement by a machine learning method,” in 10th IEEE Int. Conf. on Service Operations and Logistics, and Informatics, SOLI 2015 - In conjunction with ICT4ALL 2015, Institute of Electrical and Electronics Engineers Inc., Dec. 2015, pp. 176–181. doi: 10.1109/SOLI.2015.7367615.
  • S. Zhu, X. Lian, H. Liu, J. Hu, Y. Wang, and J. Che, “Daily air quality index forecasting with hybrid models: A case in China,” Environmental Pollution, vol. 231, pp. 1232–1244, Dec. 2017, doi: 10.1016/J.ENVPOL.2017.08.069.
  • Z. Yang and J. Wang, “A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction,” Environ Res, vol. 158, pp. 105–117, Oct. 2017, doi: 10.1016/J.ENVRES.2017.06.002.
  • W. J. Requia, B. A. Coull, and P. Koutrakis, “Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space,” Environ Res, vol. 175, pp. 421–433, Aug. 2019, doi: 10.1016/J.ENVRES.2019.05.025.
  • S. Qin, F. Liu, J. Wang, and B. Sun, “Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models,” Atmos Environ, vol. 98, pp. 665–675, Dec. 2014, doi: 10.1016/J.ATMOSENV.2014.09.046.
  • K. B. Shaban, A. Kadri, and E. Rezk, “Urban air pollution monitoring system with forecasting models,” IEEE Sens J, vol. 16, no. 8, pp. 2598–2606, Apr. 2016, doi: 10.1109/JSEN.2016.2514378.
  • L. Jian, Y. Zhao, Y. P. Zhu, M. B. Zhang, and D. Bertolatti, “An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China,” Science of The Total Environment, vol. 426, pp. 336–345, Jun. 2012, doi: 10.1016/J.SCITOTENV.2012.03.025.
  • G. Oliveri Conti, B. Heibati, I. Kloog, M. Fiore, and M. Ferrante, “A review of AirQ Models and their applications for forecasting the air pollution health outcomes,” Environmental Science and Pollution Research 2017 24:7, vol. 24, no. 7, pp. 6426–6445, Jan. 2017, doi: 10.1007/S11356-016-8180-1.
  • A. Kumar and P. Goyal, “Forecasting of air quality in Delhi using principal component regression technique,” Atmos Pollut Res, vol. 2, no. 4, pp. 436–444, Oct. 2011, doi: 10.5094/APR.2011.050.
  • C. Srivastava, S. Singh, and A. P. Singh, “Estimation of air pollution in Delhi using machine learning techniques,” 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018, pp. 304–309, Mar. 2019, doi: 10.1109/GUCON.2018.8675022.
  • N. Güler Dincer and Ö. Akkuş, “A new fuzzy time series model based on robust clustering for forecasting of air pollution,” Ecol Inform, vol. 43, pp. 157–164, Jan. 2018, doi: 10.1016/J.ECOINF.2017.12.001.
  • V. Athira, P. Geetha, R. Vinayakumar, and K. P. Soman, “DeepAirNet: Applying Recurrent Networks for Air Quality Prediction,” Procedia Comput Sci, vol. 132, pp. 1394–1403, Jan. 2018, doi: 10.1016/J.PROCS.2018.05.068.
  • M. Krishan, S. Jha, J. Das, A. Singh, M. K. Goyal, and C. Sekar, “Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India,” Air Quality, Atmosphere & Health 2019 12:8, vol. 12, no. 8, pp. 899–908, Apr. 2019, doi: 10.1007/S11869-019-00696-7.
  • M. Krishan, S. Jha, J. Das, A. Singh, M. K. Goyal, and C. Sekar, “Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India,” Air Qual Atmos Health, vol. 12, no. 8, pp. 899–908, Aug. 2019, doi: 10.1007/S11869-019-00696-7.
  • “Delhi governmemt identifies 150 air pollution hotspots - The Hindu.” https://www.thehindu.com/news/cities/Delhi/delhi-governmemt-identifies-150-air-pollution-hotspots/article36850422.ece (accessed Aug. 11, 2022).
  • M. Mulkal, R. Risawandi, and N. Aflah, “Inverse Distance Weight Spatial Interpolation for Topographic Surface 3D Modelling,” TECHSI - Jurnal Teknik Informatika, vol. 11, no. 3, pp. 385–394, Oct. 2019, doi: 10.29103/TECHSI.V11I3.1934.
  • A. T. Sree Dhevi, “Imputing missing values using Inverse Distance Weighted Interpolation for time series data,” 6th International Conference on Advanced Computing, ICoAC 2014, pp. 255–259, Aug. 2015, doi: 10.1109/ICOAC.2014.7229721.
  • K. Lu et al., “GRU-based Encoder-Decoder for Short-term CHP Heat Load Forecast,” IOP Conf Ser Mater Sci Eng, vol. 392, no. 6, p. 062173, Jul. 2018, doi: 10.1088/1757-899X/392/6/062173.
  • X. B. Jin et al., “Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization,” Energies 2021, Vol. 14, Page 1596, vol. 14, no. 6, p. 1596, Mar. 2021, doi: 10.3390/EN14061596.
  • B. Zhang, G. Zou, D. Qin, Y. Lu, Y. Jin, and H. Wang, “A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction,” Science of The Total Environment, vol. 765, p. 144507, Apr. 2021, doi: 10.1016/J.SCITOTENV.2020.144507.
  • B. Zhang, G. Zou, D. Qin, Y. Lu, Y. Jin, and H. Wang, “A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction,” Science of The Total Environment, vol. 765, p. 144507, Apr. 2021, doi: 10.1016/J.SCITOTENV.2020.144507.
  • M. T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421, 2015, doi: 10.18653/v1/d15-1166.
Еще
Статья научная
SciUp 2024 © 2024 ©