Desarrollo de un algoritmo para la predicción de tres parámetros ambientales
Palabras clave:
Predicción Ambiental, Redes Neuronales, Sensores IoT, Modelos de Predicción, Aprendizaje Automático.
Resumen
La anticipación concreta de parámetros medioambientales, generada con el uso de datos en tiempo real, es fundamental para optimizar el seguimiento y el control de la contaminación del aire y del cambio climático. En este artículo, se desarrolló un algoritmo basado en redes neuronales para predecir parámetros ambientales controlados y con datos adquiridos por sensores IoT. Se presenta una comparación y análisis de la precisión de cuatro modelos de Machine Learning: Regresión Lineal, Árbol de Decisión, Random Forest y Redes Neuronales. El objetivo principal de este manuscrito es predecir tres contaminantes críticos (ozono, monóxido de carbono y propano) y dos variables ambientales complementarias (temperatura y humedad). Los resultados revelan que los modelos de predicción de Árbol de Decisión y Random Forest son especialmente precisos para la variable humedad, mientras que las Redes Neuronales destacan en la predicción de los niveles de ozono. Este estudio demuestra la capacidad de los modelos avanzados de aprendizaje automático para manejar datos ambientales complejos. Estos hallazgos importantes se proponen como un punto de partida para ser evaluados de forma detallada en diferentes escenarios con implicaciones para la gestión ambiental y la salud pública.
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Citas
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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
Alwan, H. K., Al-Sultany, G. A., & Hussein, S. M. (2023). Air Pollution Prediction Using Machine Learning and Neural Network. 5th International Conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023, 231–234. https://doi.org/10.1109/ICITAMS57610.2023.10525306
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
Cen, H., Li, J., Liu, Z., Nie, J., & Cai, Q. (2023). Temperature prediction model of sheep barn in winter based on Neural Network. 2023 24th International Conference on Electronic Packaging Technology, ICEPT 2023. https://doi.org/10.1109/ICEPT59018.2023.10492107
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
Manocha, A., Sood, S. K., & Bhatia, M. (2024). IoT-Dew Computing-Inspired Real-Time Monitoring of Indoor Environment for Irregular Health Prediction. IEEE Transactions on Engineering Management, 71, 1669–1682. https://doi.org/10.1109/TEM.2023.3338458
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
Publicado
2024-11-07
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