Aplicaciones de la inteligencia artificial en el monitoreo y conservación ambiental: una revisión exploratoria
DOI:
https://doi.org/10.24054/raaas.v15i2.3189Palabras clave:
Inteligencia artificial, Monitoreo ambiental, Aprendizaje automático, Gestión sostenibleResumen
Este artículo explora el uso de la inteligencia artificial en la vigilancia y preservación del agua, el aire y el suelo. El análisis examinó estudios revisador por pares publicados entre 2020 y 2024, con un enfoque específico en la contribución de la inteligencia artificial a la mejora de las técnicas de gestión ambiental. El procedimiento de selección se limitó a treinta y tres investigaciones pertinentes, que se clasificaron en tres dominios principales, calidad y gestión del suelo, contaminación del aire y monitoreo ambiental, y aplicaciones de IA. Las técnicas de inteligencia artificial, incluido el aprendizaje automático y el aprendizaje profundo, muestran un gran potencial para mejorar la precisión de las predicciones y optimizar la asignación de recursos en varios campos ambientales. Los usos principales de esta tecnología son evaluar la calidad del suelo, predecir los niveles de contaminación del aire y gestionar los recursos hídricos. La integración de la IA con los métodos de monitoreo convencionales mejora la precisión y la eficacia de la gestión ambiental. Sin embargo, existen dificultades continuas para garantizar la precisión y confiabilidad de los datos, la capacidad de los modelos para aplicarse a diferentes escenarios y la integración exitosa de estos modelos en diversas situaciones. La inteligencia artificial ha demostrado su capacidad para generar cambios significativos en los campos de la vigilancia y la conservación del medio ambiente. Las investigaciones posteriores deberían dar prioridad a la ampliación de los conjuntos de datos, la incorporación de la IA a las tecnologías en desarrollo y la resolución de las consecuencias socioeconómicas, a fin de aprovechar al máximo el potencial de la IA para abordar cuestiones ambientales complejas.
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Abidi, M. H., Chintakindi, S., Rehman, A. U., & Mohammed, M. K. (2024). Performance enhancement in hydroponic and soil compound prediction by deep learning techniques. PeerJ Computer Science, 10, e2101. https://doi.org/10.7717/peerj-cs.2101 DOI: https://doi.org/10.7717/peerj-cs.2101
Alghieth, M., Alawaji, R., Saleh, S. H., & Alharbi, S. (2021). Air Pollution Forecasting Using Deep Learning. International Journal of Online and Biomedical Engineering (iJOE), 17(14), Article 14. https://doi.org/10.3991/ijoe.v17i14.27369 DOI: https://doi.org/10.3991/ijoe.v17i14.27369
B. Karthikeyan, R.Mohanasundaram, P.Suresh, & Karthikeyan. (2023). Deep Learning and Machine Learning based Air Pollution Prediction Model for Smart Environment Design Planning. Global NEST Journal. https://doi.org/10.30955/gnj.004735 DOI: https://doi.org/10.30955/gnj.004735
Bammou, Y., Benzougagh, B., Abdessalam, O., Brahim, I., Kader, S., Spalevic, V., Sestras, P., & Ercisli, S. (2024). Machine learning models for gully erosion susceptibility assessment in the Tensift catchment, Haouz Plain, Morocco for sustainable development. Journal of African Earth Sciences, 213, 105229. https://doi.org/10.1016/j.jafrearsci.2024.105229 DOI: https://doi.org/10.1016/j.jafrearsci.2024.105229
Cao, Y., Ai, Z., Dang, X., Hou, M., Liu, H., Li, Q., Yao, Y., Deng, Y., Zhu, S., & Xiao, L. (2024). Effects of different reclamation measures on soil quality restoration in open-pit mines: A meta-analysis based on the Chinese Loess Plateau. Ecological Engineering, 203, 107257. https://doi.org/10.1016/j.ecoleng.2024.107257 DOI: https://doi.org/10.1016/j.ecoleng.2024.107257
Chang, F.-J., Chang, L.-C., & Chen, J.-F. (2023). Artificial Intelligence Techniques in Hydrology and Water Resources Management. Water, 15(10), Article 10. https://doi.org/10.3390/w15101846 DOI: https://doi.org/10.3390/w15101846
dos Santos, D. S., Ribeiro, P. G., Andrade, R., Silva, S. H. G., Gastauer, M., Caldeira, C. F., Guedes, R. S., Dias, Y. N., Souza Filho, P. W. M., & Ramos, S. J. (2024). Clean and accurate soil quality monitoring in mining areas under environmental rehabilitation in the Eastern Brazilian Amazon. Environmental Monitoring and Assessment, 196(4), 385. https://doi.org/10.1007/s10661-024-12495-4 DOI: https://doi.org/10.1007/s10661-024-12495-4
Dutta, A., Sampson, J., Ahmad, S., Avudaiappan, T., Narayanasamy, K., Pustokhina, I., & Pustokhin, D. (2022). Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment. Computers, Materials & Continua, 72(1), 1157-1172. https://doi.org/10.32604/cmc.2022.024109 DOI: https://doi.org/10.32604/cmc.2022.024109
El Behairy, R. A., El Arwash, H. M., El Baroudy, A. A., Ibrahim, M. M., Mohamed, E. S., Rebouh, N. Y., & Shokr, M. S. (2024). An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands. Agriculture, 14(4), Article 4. https://doi.org/10.3390/agriculture14040627 DOI: https://doi.org/10.3390/agriculture14040627
El-Rawy, M., Sayed, S. Y., AbdelRahman, M. A. E., Makhloof, A., Al-Arifi, N., & Abd-Ellah, M. K. (2024). Assessing and segmenting salt-affected soils using in-situ EC measurements, remote sensing, and a modified deep learning MU-NET convolutional neural network. Ecological Informatics, 81, 102652. https://doi.org/10.1016/j.ecoinf.2024.102652 DOI: https://doi.org/10.1016/j.ecoinf.2024.102652
Fadl, M. E., Sayed, Y. A., El-Desoky, A. I., Shams, E. M., Zekari, M., Abdelsamie, E. A., Drosos, M., & Scopa, A. (2024). Irrigation Practices and Their Effects on Soil Quality and Soil Characteristics in Arid Lands: A Comprehensive Geomatic Analysis. Soil Systems, 8(2), Article 2. https://doi.org/10.3390/soilsystems8020052 DOI: https://doi.org/10.3390/soilsystems8020052
Guo, Q., Ren, M., Wu, S., Sun, Y., Wang, J., Wang, Q., Ma, Y., Song, X., & Chen, Y. (2022). Applications of artificial intelligence in the field of air pollution: A bibliometric analysis. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.933665 DOI: https://doi.org/10.3389/fpubh.2022.933665
Jain, S., Sethia, D., & Tiwari, K. C. (2024). A critical systematic review on spectral-based soil nutrient prediction using machine learning. Environmental Monitoring and Assessment, 196(8), 699. https://doi.org/10.1007/s10661-024-12817-6 DOI: https://doi.org/10.1007/s10661-024-12817-6
Krishnan, S. R., Nallakaruppan, M. K., Chengoden, R., Koppu, S., Iyapparaja, M., Sadhasivam, J., & Sethuraman, S. (2022). Smart Water Resource Management Using Artificial Intelligence—A Review. Sustainability, 14(20), Article 20. https://doi.org/10.3390/su142013384 DOI: https://doi.org/10.3390/su142013384
Krupnova, T. G., Rakova, O. V., Bondarenko, K. A., & Tretyakova, V. D. (2022). Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring. Big Data and Cognitive Computing, 6(3), Article 3. https://doi.org/10.3390/bdcc6030075 DOI: https://doi.org/10.3390/bdcc6030075
Latif, S. D., & Ahmed, A. N. (2023). A review of deep learning and machine learning techniques for hydrological inflow forecasting. Environment, Development and Sustainability, 25(11), 12189-12216. https://doi.org/10.1007/s10668-023-03131-1 DOI: https://doi.org/10.1007/s10668-023-03131-1
Lima, H. S., Oliveira, G. F. V. de, Ferreira, R. dos S., Castro, A. G. de, Silva, L. C. F., Ferreira, L. de S., Oliveira, D. A. dos S., Silva, L. F. da, Kasuya, M. C. M., de Paula, S. O., & Silva, C. C. da. (2024). Machine learning-based soil quality assessment for enhancing environmental monitoring in iron ore mining-impacted ecosystems. Journal of Environmental Management, 356, 120559. https://doi.org/10.1016/j.jenvman.2024.120559 DOI: https://doi.org/10.1016/j.jenvman.2024.120559
Liu, Y., Huang, X., & Liu, Y. (2024). Detection of long-term land use and ecosystem services dynamics in the Loess Hilly-Gully region based on artificial intelligence and multiple models. Journal of Cleaner Production, 447, 141560. https://doi.org/10.1016/j.jclepro.2024.141560 DOI: https://doi.org/10.1016/j.jclepro.2024.141560
Neo, E. X., Hasikin, K., Lai, K. W., Mokhtar, M. I., Azizan, M. M., Hizaddin, H. F., Razak, S. A., & Yanto. (2023). Artificial intelligence-assisted air quality monitoring for smart city management. PeerJ Computer Science, 9, e1306. https://doi.org/10.7717/peerj-cs.1306 DOI: https://doi.org/10.7717/peerj-cs.1306
Omri, T., Karoui, A., Georges, D., & Ayadi, M. (2024). Evaluation of hybrid deep learning approaches for air pollution forecasting. International Journal of Environmental Science and Technology, 21(11), 7445-7466. https://doi.org/10.1007/s13762-024-05644-2 DOI: https://doi.org/10.1007/s13762-024-05644-2
Organization, W. H. (2014). 7 million premature deaths annually linked to air pollution. https://www.who.int/news/item/25-03-2014-7-million-premature-deaths-annually-linked-to-air-pollution
Oriol, T., Pasquet, J., & Cortet, J. (2024). Automatic identification of Collembola with deep learning techniques. Ecological Informatics, 81, 102606. https://doi.org/10.1016/j.ecoinf.2024.102606 DOI: https://doi.org/10.1016/j.ecoinf.2024.102606
Ou, J., Wu, Z., Yan, Q., Feng, X., & Zhao, Z. (2024). Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information. Environmental Sciences Europe, 36(1), 80. https://doi.org/10.1186/s12302-024-00912-x DOI: https://doi.org/10.1186/s12302-024-00912-x
Pacci, S., Dengiz, O., Alaboz, P., & Saygin, F. (2024). Artificial neural networks in soil quality prediction: Significance for sustainable tea cultivation. Science of The Total Environment, 947, 174447. https://doi.org/10.1016/j.scitotenv.2024.174447 DOI: https://doi.org/10.1016/j.scitotenv.2024.174447
Paliwal, A., Mhelezi, M., Galgallo, D., Banerjee, R., Malicha, W., & Whitbread, A. (2024). Utilizing Artificial Intelligence and Remote Sensing to Detect Prosopis juliflora Invasion: Environmental Drivers and Community Insights in Rangelands of Kenya. Plants, 13(13), Article 13. https://doi.org/10.3390/plants13131868 DOI: https://doi.org/10.3390/plants13131868
Prakash, A. J., Begam, S., Vilímek, V., Mudi, S., & Das, P. (2024). Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques. Geoenvironmental Disasters, 11(1), 14. https://doi.org/10.1186/s40677-024-00275-8 DOI: https://doi.org/10.1186/s40677-024-00275-8
Sow, S., Ranjan, S., Seleiman, M., Alkharabsheh, H., Kumar, M., Kumar, N., Padhan, S., Roy, D., Nath, D., Gitari, H., & Wasonga, D. (2024). Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies. Phyton-International Journal of Experimental Botany, 93(7), 1569-1598. https://doi.org/10.32604/phyton.2024.052241 DOI: https://doi.org/10.32604/phyton.2024.052241
Subramaniam, S., Raju, N., Ganesan, A., Rajavel, N., Chenniappan, M., Prakash, C., Pramanik, A., Basak, A. K., & Dixit, S. (2022). Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review. Sustainability, 14(16), Article 16. https://doi.org/10.3390/su14169951 DOI: https://doi.org/10.3390/su14169951
Thabit, F. N., Negim, O. I. A., AbdelRahman, M. A. E., Scopa, A., & Moursy, A. R. A. (2024). Using Various Models for Predicting Soil Organic Carbon Based on DRIFT-FTIR and Chemical Analysis. Soil Systems, 8(1), Article 1. https://doi.org/10.3390/soilsystems8010022 DOI: https://doi.org/10.3390/soilsystems8010022
Verjans, V., & Robel, A. (2024). Accelerating Subglacial Hydrology for Ice Sheet Models With Deep Learning Methods. Geophysical Research Letters, 51(2), e2023GL105281. https://doi.org/10.1029/2023GL105281 DOI: https://doi.org/10.1029/2023GL105281
Vidnerová, P., & Neruda, R. (2021). Air Pollution Modelling by Machine Learning Methods. Modelling, 2(4), Article 4. https://doi.org/10.3390/modelling2040035 DOI: https://doi.org/10.3390/modelling2040035
Yu, X., Chen, S., Zhang, X., Wu, H., Guo, Y., & Guan, J. (2023). Research progress of the artificial intelligence application in wastewater treatment during 2012–2022: A bibliometric analysis. Water Science and Technology, 88(7), 1750-1766. https://doi.org/10.2166/wst.2023.296 DOI: https://doi.org/10.2166/wst.2023.296
Zareba, M., Dlugosz, H., Danek, T., & Weglinska, E. (2023). Big-Data-Driven Machine Learning for Enhancing Spatiotemporal Air Pollution Pattern Analysis. Atmosphere, 14(4), Article 4. https://doi.org/10.3390/atmos14040760 DOI: https://doi.org/10.3390/atmos14040760
Zhu, M., Ai, X., Sheng, M., Ai, S., Miao, N., & Ai, Y. (2024). Using a soil quality index to evaluate the artificial soil quality response to rehabilitation time. Ecological Engineering, 202, 107234. https://doi.org/10.1016/j.ecoleng.2024.107234 DOI: https://doi.org/10.1016/j.ecoleng.2024.107234
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Derechos de autor 2024 REVISTA AMBIENTAL AGUA, AIRE Y SUELO
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
