Applications of artificial intelligence in environmental monitoring and conservation: an exploratory review
DOI:
https://doi.org/10.24054/raaas.v15i2.3189Keywords:
Artificial Intelligence, Environmental Monitoring, Machine Learning, Sustainable ManagementAbstract
This paper explores the utilization of artificial intelligence (AI) in the surveillance and preservation of water, air, and soil. The analysis examined peer-reviewed studies published between 2020 and 2024, with a specific focus on the contribution of AI to the improvement of environmental management techniques. The selection procedure was limited down to thirty-three pertinent research, which were classified into three primary domains: Soil Quality and Management, Air Pollution and Environmental Monitoring, and AI Applications. Artificial intelligence techniques, including machine learning and deep learning, show great promise in enhancing the accuracy of predictions and optimizing the allocation of resources in several environmental fields. The primary uses of this technology are to evaluate the quality of soil, predict air pollution levels, and manage water resources. Integrating AI with conventional monitoring methods improves the accuracy and effectiveness of environmental management. Nevertheless, there are ongoing difficulties in ensuring the accuracy and reliability of data, the capacity of models to apply to different scenarios, and the successful integration of these models in various situations. Artificial intelligence demonstrates the ability to bring about significant changes in the fields of environmental monitoring and conservation. Subsequent investigations should prioritize the enlargement of datasets, the incorporation of AI with developing technologies, and the resolution of socio-economic consequences to fully connect the potential of AI in addressing intricate environmental concerns.
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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Vidnerová, P., & Neruda, R. (2021). Air Pollution Modelling by Machine Learning Methods. Modelling, 2(4), Article 4. 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
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
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
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