Development of an algorithm for the prediction of three environmental parameters
Keywords:
Environmental Prediction, Neural Networks, IoT Sensors, Prediction Models, Machine Learning
Abstract
The concrete anticipation of environmental parameters, generated with the use of real-time data, is essential to optimize the monitoring and control of air pollution and climate change. In this paper, an algorithm based on neural networks was developed to predict controlled environmental parameters and with data acquired by IoT sensors. A comparison and analysis of the accuracy of four Machine Learning models Linear Regression, Decision Tree, Random Forest and Neural Networks is presented. The main objective of this manuscript is to predict three critical pollutants (ozone, carbon monoxide and propane) and two complementary environmental variables (temperature and humidity). The results reveal that Decision Tree and Random Forest prediction models are especially accurate for the humidity variable, while Neural Networks excel in the prediction of ozone levels. This study demonstrates the ability of advanced machine learning models to handle complex environmental data. These important findings are proposed as a starting point to be evaluated in detail in different scenarios with implications for environmental management and public health.
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References
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Baranitharan, B., Sivapragasam, C., & Rajesh, K. (2021). Energy Demand Prediction Considering Weather Change Effects using Artificial Neural Network. Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, 916–921. https://doi.org/10.1109/ICAC3N53548.2021.9725452
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Chen, J., Wei, X., Liu, Y., Liu, Z., Ma, Y., Zhang, M., Guo, Y., Bao, Z., & Zhang, H. (2023). Prediction of Water Quality of Xincheng Bridge in Lanzhou Based on CNN-LSTM Model. Proceedings - 2023 3rd International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2023, 59–63. https://doi.org/10.1109/ISPCEM60569.2023.00018
Chen, Y., Song, L., Liu, Y., Yang, L., & Li, D. (2020). A review of the artificial neural network models for water quality prediction. In Applied Sciences (Switzerland) (Vol. 10, Issue 17). https://doi.org/10.3390/app10175776
Ding, S., Yang, H., Wang, Z., Song, G., Peng, Y., & Peng, X. (2018). Dynamic Prediction of the Silicon Content in the Blast Furnace using LSTM-RNN-Based Models. Proceedings of 2018 International Computers, Signals and Systems Conference, ICOMSSC 2018, 491–495. https://doi.org/10.1109/ICOMSSC45026.2018.8941807
Frolov, S. V., Potlov, A. Y., Korobov, A. A., & Savinova, K. S. (2023). Neural Network Control of Environmental Parameters in Neonatal Incubators. Proceedings of 2023 4th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2023, 21–24. https://doi.org/10.1109/NEURONT58640.2023.10175837
Huu, K. D. (2024). Research on Ship Weather Routing Method Based on Dijkstra Algorithm and Neural Network. Proceedings - 2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024, 739–744. https://doi.org/10.1109/ICIEAM60818.2024.10553838
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Pandya, M., Dave, V., & Ghosh, R. (2020). Artificial neural network (ANN) based soil electrical conductivity (SEC) prediction. 2020 7th International Conference on Signal Processing and Integrated Networks, SPIN 2020, 581–586. https://doi.org/10.1109/SPIN48934.2020.9071257
Popli, N., Davoodi, E., Capitanescu, F., & Wehenkel, L. (2024). Machine Learning Based Binding Contingency Pre-Selection for AC-PSCOPF Calculations. IEEE Transactions on Power Systems, 39(2), 4751–4754. https://doi.org/10.1109/TPWRS.2023.3338971
Rao, D. V. S. K. K., Prusty, B. R., & Myneni, H. (2022). Bright Sunshine Duration Index-Based Prediction of Solar PV Power Using ANN Approach. 2022 International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022. https://doi.org/10.1109/ICICCSP53532.2022.9862452
Sathyaseelan, K., & Sarathambekai, S. (2021). Machine Learning based Prediction Model for Health Care Sector - A Survey. 3rd IEEE International Virtual Conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021. https://doi.org/10.1109/I-PACT52855.2021.9696646
Shu, B., Zhang, W., Chen, Y., Sun, J., & Wang, C. X. (2024). Path Loss Prediction in Evaporation Ducts Based on Deep Neural Network. IEEE Antennas and Wireless Propagation Letters, 23(2), 798–802. https://doi.org/10.1109/LAWP.2023.3336469
Shukla, A., Sharma, M., Tiwari, K., Vani, V. D., Kumar, N., & Pooja. (2023). Predicting Rainfall Using an Artificial Neural Network-Based Model. Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, 2700–2704. https://doi.org/10.1109/IC3I59117.2023.10397714
Suleman, M. A. R., & Shridevi, S. (2022). Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model. IEEE Access, 10, 82456–82468. https://doi.org/10.1109/ACCESS.2022.3196381
Wu, L., He, D., Guan, K., Ai, B., Briso-Rodriguez, C., Shui, T., Liu, C., Zhu, L., & Shen, X. (2020). Received Power Prediction for Suburban Environment based on Neural Network. International Conference on Information Networking, 2020-January, 35–39. https://doi.org/10.1109/ICOIN48656.2020.9016532
Yang, H., Yu, H., Zheng, K., Hu, J., Tao, T., & Zhang, Q. (2023). Hyperspectral Image Classification Based on Interactive Transformer and CNN With Multilevel Feature Fusion Network. IEEE Geoscience and Remote Sensing Letters, 20. https://doi.org/10.1109/LGRS.2023.3303008
Yang, J., Chen, H., Lin, S., Chen, L., & Chen, Y. (2022). Prediction of temperature change with multi-dimensional environmental characteristic based on CNN-LSTM-ATTENTION model. IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2022-June, 1024–1029. https://doi.org/10.1109/ITAIC54216.2022.9836837
Published
2024-11-07
Section
Artículos
