Common Issues

We have read all the listed references and found a common issue along all of them:

  1. All artificial neural nets are based on perceptron-based architecture, but one that uses LSTM (Singh, Narasimhan, and Lakshminarayanan 2019). None of the papers state clearly the architecture.

  2. All use, mainly MATLAB. Instead of Keras1 , Tensorflow2 , scikit-learn3 or PyTorch4 .

  3. We did not find any code or data published, but it is possible to be available upon request. This is a big issue for reproducibility and replicability.

  4. We didn’t find any outstanding results for predicting any weather or polution in any of the papers cited.

It is natural to build and improve upon these weaknesses and address them. Therefore, we may have a great chance of having our paper published in a peer-reviewed journal.

Common Points

  1. Most relevant papers in the computer science field are published by the IEEE.5 Those that focus on the environmental field tend to be published in Environmental and Polution Conferences.

  2. Regulatory bodies play an important part of the polution and environmental papers. E.g., EPA6 , ANSI7 , ..

  3. Most of the papers are published by universities and non-for-profit organizations.

  4. Surprisingly, many articles come from Asia (Hong Kong, Malaysia, India) and the U.S. entities prominently regulate and are a references from a compliance and safety levels standpoint.

References

Abdullah, Akibu Mahmoud, Raja Sher Afgun Usmani, Thulasyammal Ramiah Pillai, Mohsen Marjani, and Ibrahim Abaker Targio Hashem. 2021. “An Optimized Artificial Neural Network Model Using Genetic Algorithm for Prediction of Traffic Emission Concentrations.” International Journal of Advanced Computer Science and Applications 12 (6). https://doi.org/10.14569/ijacsa.2021.0120693.

Cakir, Sedef, and Moro Sita. 2020. “Evaluating the Performance of Ann in Predicting the Concentrations of Ambient Air Pollutants in Nicosia.” Atmospheric Pollution Research 11 (12): 2327–34. https://doi.org/10.1016/j.apr.2020.06.011.

Ghazali, Suraya, and Lokman Hakim Ismail. 2012. “Air Quality Prediction Using Artificial Neural Network.” Universiti Tun Hussein Onn Malaysia. http://eprints.uthm.edu.my/2528/1/Air_Quality_Prediction_Using_Artificial_Neural_Network.pdf.

Guidance Notes for the Management of Indoor Air Quality in Offices and Public Places. 2003. Indoor Air Quality Management Group The Government of the Hong Kong Special Administrative Region.

Guidelines for the Reporting of Daily Air Quality-the Air Quality Index (Aqi). 2006. United States Environmental Protection Agency, Office of Air Quality Planning; Standards.

Haddad, Rafi, Abebe Medhanie, Yehudah Roth, David Harel, and Noam Sobel. 2010. “Predicting Odor Pleasantness with an Electronic Nose.” PLoS Computational Biology 6 (4). https://doi.org/10.1371/journal.pcbi.1000740.

Industry Code of Practice on Indoor Air Quality 2010. 2010. Department of Occupational Safety; Health - Ministry of Human Resources Malaysia.

Patni, Jagdish Chandra, and Hitesh Kumar Sharma. 2019. “Air Quality Prediction Using Artificial Neural Networks.” 2019 International Conference on Automation, Computational and Technology Management (ICACTM). https://doi.org/10.1109/icactm.2019.8776774.

Rahman, Nur Haizum Abd, Muhammad Hisyam Lee, Mohd Talib Latif, and Suhartono S. 2013. “Forecasting of Air Pollution Index with Artificial Neural Network.” Jurnal Teknologi 63 (2). https://doi.org/10.11113/jt.v63.1913.

Saad, Shaharil, Allan Andrew, Ali Shakaff, Abdul Saad, Azman Kamarudin, and Ammar Zakaria. 2015. “Classifying Sources Influencing Indoor Air Quality (Iaq) Using Artificial Neural Network (Ann).” Sensors 15 (5): 11665–84. https://doi.org/10.3390/s150511665.

Saad, Shaharil Mad, Ali Yeon Md Shakaff, Abdul Rahman Mohd Saad, and Azman Muhamad Yusof Kamarudin. 2014. “Implementation of Index for Real-Time Monitoring Indoor Air Quality System.” 2014 2nd International Conference on Electronic Design (ICED). https://doi.org/10.1109/iced.2014.7015770.

Singh, Pratyush, T Lakshmi Narasimhan, and Chandra Shekar Lakshminarayanan. 2019. “DeepAir: Air Quality Prediction Using Deep Neural Network.” TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). https://doi.org/10.1109/tencon.2019.8929470.

Ventilation for Acceptable Indoor Air Quality: ASHRAE Standard. 2013. American Society of Heating, Refrigerating; Air-Conditioning Engineers.

Xie, Hui, Fei Ma, and Qingyuan Bai. 2009. “Prediction of Indoor Air Quality Using Artificial Neural Networks.” 2009 Fifth International Conference on Natural Computation. https://doi.org/10.1109/icnc.2009.502.

  1. https://keras.io↩︎

  2. https://www.tensorflow.org/↩︎

  3. https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neural_network↩︎

  4. https://pytorch.org/↩︎

  5. Institute of Electrical and Electronics Engineers↩︎

  6. U.S. Environmental Protection Agency↩︎

  7. American National Standards Institute↩︎