PROGRAMMING LANGUAGE SELECTION FOR INTELLIGENT FOREST MONITORING AND FIRE-FIGHTING SYSTEMS USING THE ANALYTIC HIERARCHY PROCESS
Keywords:
Analytic Hierarchy Process, Intelligent Monitoring Systems, Forest Fire Monitoring, Artificial Intelligence, programming language Rust, programming language Python, Deep Learning, Real-time Processing, Edge Computing, Multi-Criteria Decision MakingAbstract
This paper addresses the critical issue of selecting the optimal programming language for developing intelligent environmental monitoring systems operating in real-time. The study emphasizes the transition from passive observation to the active use of artificial intelligence algorithms (Computer Vision, Deep Learning) for the early detection of forest fires. The scientific novelty lies in the application of the mathematical framework of the Analytic Hierarchy Process (AHP) to minimize subjectivity in the selection of the technology stack. The authors constructed a hierarchical evaluation model for four alternatives: Python, C++, Rust, and Lisp.For the comparative analysis, five key criteria were selected: processing speed, recognition accuracy, autonomy (energy efficiency), scalability, and development cost. The calculation of the criteria weight coefficients demonstrated the priority of detection accuracy (43%) and real-time system performance (28%). Based on the integral indicators, the Rust language (8.54) showed clear superiority over C++ (7.82) and Python (7.16). It was established that Rust is the most balanced solution for implementing the Edge Computing concept, as it ensures memory safety and high processing speeds directly on autonomous devices. Consequently, an optimal hybrid architecture strategy is proposed: utilizing Rust for low-level detection modules and Python for cloud data analytics and visualization. This approach allows for a shift from subjective tool selection to objective mathematical justification based on critical safety requirements.
References
1. Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
2. Saaty, T. L. (2003). Decision making with the analytic hierarchy process: Why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85–91. https://doi.org/10.1016/S0377-2217(02)00227-8.
3. Saaty, T. L. (2008). Decision making for leaders: The analytic hierarchy process for decisions in a complex world (Rev. ed.). RWS Publications.
4. Goepel, K. D. (2013). Implementing the analytic hierarchy process as a standard method for multi-criteria decision making in corporate enterprises. In Proceedings of the International Symposium on the Analytic Hierarchy Process. BPMSG.
5. Goepel, K. D. (n.d.). Analytic hierarchy process (AHP) tutorial. Business Performance Management Singapore. Retrieved October 24, 2023, from https://bpmsg.com/ahp-introduction/
6. Ishizaka, A., & Labib, A. (2011). Review of the main developments of the analytic hierarchy process. Expert Systems with Applications, 38(11), 14036–14039. https://doi.org/10.1016/j.eswa.2011.04.143.
7. Velasquez, M., & Hester, P. T. (2021). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56–66.
8. Malefaki, S., Markatos, D., Filippatos, A., & Pantelakis, S. G. (2025). A comparative analysis of multi-criteria decision-making methods and normalization techniques in holistic sustainability assessment for engineering applications. Aerospace, 12(2), 100. https://doi.org/10.3390/aerospace12020100.
9. Jähne, B. (2022). Digital image processing: Concepts, algorithms, and scientific applications (3rd ed.). Springer. https://doi.org/10.1007/978-3-662-03174-2.
10. Saleh, A., Zulkifley, M. A., Harun, H. H., Gaudreault, F., Davison, I., & Spraggon, M. (2024). Forest fire surveillance systems: A review of deep learning methods. Heliyon, 10(1), Article e23127. https://doi.org/10.1016/j.heliyon.2023.e23127.
11. Bustamante, A., Belmonte, L. M., Morales, R., Pereira, A., & Fernández-Caballero, A. (2022). Video Processing from a Virtual Unmanned Aerial Vehicle: Comparing Two Approaches to Using OpenCV in Unity. Applied Sciences, 12(12), 5958. https://doi.org/10.3390/app12125958.
12. Vargas, R. V. (2010). Using the analytic hierarchy process (AHP) to select and prioritize projects in a portfolio. In Paper presented at PMI® Global Congress 2010—North America, Washington, DC. Project Management Institute. https://www.pmi.org/learning/library/analytic-hierarchy-process-prioritize-projects-6608.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Information technologies in economics and environmental sciences
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
