Aleksandar Petrovski1
Rexhep Mustafovski1
Marko Radovanović2
1‘University Goce Delcev’ – Stip, Military Academy “General Mihailo Apostolski”, Republic of North Macedonia
2Military Academy, University of Defence, Belgrade, Republic of Serbia
DOI:
UDC: [910:004.6/.8]:502.17-047.36
Published: August 2026
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Keywords: Geographic Information Systems (GIS); Artificial Intelligence; Environmental Monitoring; Remote Sensing; Natural Resource Management; Smart Environmental Systems; Sustainable Development
Abstract:
The growing complexity of environmental systems demands advanced analytical approaches that integrate spatial data processing with intelligent decision-making. Geographic Information Systems (GIS) provide essential capabilities for managing and visualizing environmental data, but their analytical potential is significantly enhanced when combined with artificial intelligence (AI). This paper proposes an integrated GIS–AI framework for intelligent environmental monitoring and natural resource management. The approach incorporates multi-layer geospatial data, remote sensing, unmanned aerial vehicles, and sensor-based observations to support real-time analysis and predictive modeling. Artificial intelligence techniques are applied to identify spatial patterns, assess environmental risks, and support scenario-based decision making. The proposed framework improves scalability, adaptability, and analytical efficiency compared to conventional GIS-based systems, demonstrating its potential for sustainable environmental protection and resource management.
Chen, L.; Chen, Z.; Zhang, Y.; et al. Artificial intelligence-based solutions for climate change: A review (Retracted Article). Environmental Chemistry Letters, 21, 2525–2557, 2023. https://doi.org/10.1007/s10311-023-01617-y
National Geographic. GIS (Geographic Information System). Available online: https://education.nationalgeographic.org/resource/geographic-information-system-gis (accessed on 26 January 2026).
Abdalla, A.N.; Nazir, M.S.; Tao, H.; Cao, S.; Ji, R.; Jiang, M.; Yao, L. Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview. Journal of Energy Storage 2021, 40, 102811. https://doi.org/10.1016/j.est.2021.102811
Abduljabbar, R.; Dia, H.; Liyanage, S.; Bagloee, S.A. Applications of artificial intelligence in transport: An overview. Sustainability 2019, 11, 189. https://doi.org/10.3390/su11010189
Adikari, K.E.; Shrestha, S.; Ratnayake, D.T.; Budhathoki, A.; Mohanasundaram, S.; Dailey, M.N. Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions. Environmental Modelling & Software 2021, 144, 105136. https://doi.org/10.1016/j.envsoft.2021.105136
Afzaal, H.; Farooque, A.A.; Abbas, F.; Acharya, B.; Esau, T. Computation of evapotranspiration with artificial intelligence for precision water resource management. Applied Sciences 2020, 10, 1621. https://doi.org/10.3390/app10051621
Petrovski, A.; Radovanović, M.; Behlić, A. Application of drones with artificial intelligence for military purposes. In Proc. 10th Int. Sci. Conf. Defensive Technol. (OTEH), 2022, 92-100.
Ahmad, T.; Zhang, D.; Huang, C.; Zhang, H.; Dai, N.; Song, Y.; Chen, H. Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production 2021, 289, 125834. https://doi.org/10.1016/j.jclepro.2021.125834
Ahmad, T.; Zhu, H.; Zhang, D.; Tariq, R.; Bassam, A.; Ullah, F.; AlGhamdi, A.S.; Alshamrani, S.S. Energetics systems and artificial intelligence: Applications of Industry 4.0. Energy Reports 2022, 8, 334–361. https://doi.org/10.1016/j.egyr.2021.11.256
Ahmed, Q.W.; Garg, S.; Rai, A.; Ramachandran, M.; Jhanjhi, N.Z.; Masud, M.; Baz, M. AI-based resource allocation techniques in wireless sensor IoT networks for energy efficiency with data optimization. Electronics 2022, 11, 2071. https://doi.org/10.3390/electronics11132071
Ahmed, S.; Alshater, M.M.; Ammari, A.E.; Hammami, H. Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance 2022, 61, 101646. https://doi.org/10.1016/j.ribaf.2022.101646
Radovanović, M.; Petrovski, A.; Behlić, A.; Mustafovski, R.; Ilievski, K.; Jokić, Ž.; Ackovska, S. Angle Based Computer Vision Target Locallization Module for Enhanced Military Surveillance. Scientific Technical Review, 2024, 74(2), 32-37. https://doi.org/10.5937/OTEH2402032R
Alassery, F.; Alzahrani, A.; Khan, A.I.; Irshad, K.; Islam, S. An artificial intelligence-based solar radiation prediction model for green energy utilization in energy management systems. Sustainable Energy Technologies and Assessments 2022, 52, 102060. https://doi.org/10.1016/j.seta.2022.102060
AlDousari, A.E.; Kafy, A.A.; Saha, M.; Fattah, M.A.; Almulhim, A.I.; Faisal, A.A.; Al Rakib, A.; Jahir, D.M.A.; Rahaman, Z.A.; Bakshi, A.; Shahrier, M.; Rahman, M.M. Modelling the impacts of land use/land cover change on urban thermal characteristics in Kuwait. Sustainable Cities and Society 2022, 86, 104107. https://doi.org/10.1016/j.scs.2022.104107
Alem, A.; Kumar, S. Transfer learning models for land cover and land use classification in remote sensing images. Applied Artificial Intelligence 2022, 36, 2014192. https://doi.org/10.1080/08839514.2021.2014192
[
Alexandru, M.; Dragoș, C.; Bălă-Constantin, Z. Digital twin for automated guided vehicles fleet management. Procedia Computer Science 2022, 199, 1363–1369. https://doi.org/10.1016/j.procs.2022.01.172
Alimissis, A.; Philippopoulos, K.; Tzanis, C.G.; Deligiorgi, D. Spatial estimation of urban air pollution using artificial neural network models. Atmospheric Environment 2018, 191, 205–213. https://doi.org/10.1016/j.atmosenv.2018.07.058
Allam, Z.; Dhunny, Z.A. On big data, artificial intelligence and smart cities. Cities 2019, 89, 80–91. https://doi.org/10.1016/j.cities.2019.01.032
Almalawi, A.; Alsolami, F.; Khan, A.I.; Alkhathlan, A.; Fahad, A.; Irshad, K.; Qaiyum, S.; Alfakeeh, A.S. An IoT-based system for enhanced air pollution monitoring and prediction using hybrid artificial intelligence. Environmental Research 2022, 206, 112576. https://doi.org/10.1016/j.envres.2021.112576
Al-Othman, A.; Tawalbeh, M.; Martis, R.; Dhou, S.; Orhan, M.; Qasim, M.; Olabi, A.G. Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells. Energy Conversion and Management 2022, 253, 115154. https://doi.org/10.1016/j.enconman.2021.115154
Ampatzidis, Y.; Partel, V.; Costa, L. Agroview: A cloud-based application for UAV data processing and visualization using artificial intelligence. Computers and Electronics in Agriculture 2020, 174, 105457. https://doi.org/10.1016/j.compag.2020.105457
An, Y.; Chen, T.; Shi, L.; Heng, C.K.; Fan, J. Solar energy potential using GIS-based urban residential environmental data: A case study of Shenzhen, China. Sustainable Cities and Society 2023, 93, 104547. https://doi.org/10.1016/j.scs.2023.104547
Mustafovski, R.; Petrovski, A.; Radovanovic, M. Integrating quantum technologies into mobile military systems and TOC frameworks. Land Forces Academy Review, 2025, 30(3), 466-478. https://doi.org/10.2478/raft-2025-0045
Arumugam, K.; Swathi, Y.; Sanchez, D.T.; Mustafa, M.; Phoemchalard, C.; Phasinam, K.; Okoronkwo, E. Applicability of machine learning techniques in agriculture and energy sectors. Materials Today: Proceedings 2022, 51, 2260–2263. https://doi.org/10.1016/j.matpr.2021.11.394
Ashfaq, A.; Kamran, M.; Rehman, F.; Sarfaraz, N.; Ilyas, H.U.; Riaz, H.H. Role of artificial intelligence in renewable energy and future prospects. In Proceedings of the 5th International Conference on Energy Conservation and Efficiency (ICECE), 2022; pp. 1–6. https://doi.org/10.1109/ICECE54634.2022.9758957
Ding, Z.; Chen, Z.; Liu, J.; Evrendilek, F.; He, Y.; Xie, W. Co-combustion, life-cycle circularity, and artificial intelligence-based multi-objective optimization of two plastics and textile dyeing sludge. Journal of Hazardous Materials 2022, 426, 128069. https://doi.org/10.1016/j.jhazmat.2021.128069
Dominguez, D.; del Villar, L.D.; Pantoja, O.; González-Rodríguez, M. Forecasting Amazon rainforest deforestation using a hybrid machine learning model. Sustainability 2022, 14, 691. https://doi.org/10.3390/su14020691