Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment
<p>Overview of the paper’s work. Label (<b>1</b>) represents “Filter off”, and Label (<b>2</b>) represents “Filter on”.</p> "> Figure 2
<p>Correlation matrix of variables.</p> "> Figure 3
<p>ROC curve.</p> "> Figure 4
<p>The task area. The white dot icons represent UAV’s patrol points, the blue icon represents UAV, and the question mark icon represents UAV’s path planning process.</p> "> Figure 5
<p>The path results for different methods applied to the same data: (<b>a</b>) Group 1 (conventional method), (<b>b</b>) Group 2, (<b>c</b>) Group 3, and (<b>d</b>) Group 4 (our method). The numbers adjacent to the coordinate points denote the probability of a fire occurring at each point.</p> "> Figure 6
<p>Comparison of (<b>a</b>) path lengths and (<b>b</b>) fire spread areas by different methods.</p> "> Figure 7
<p>Fire spread areas at points reached by different methods. The circles are darker further to the left, representing higher fire probabilities and emphasizing the need for stronger fire prevention measures.</p> "> Figure 8
<p>The paths derived using different methods for the same data. (<b>a</b>) Group 1 (conventional method), (<b>b</b>) Groups 3 and 4 (filtering method). The numbers next to each coordinate point denote the probability of a fire occurring at that location.</p> "> Figure 9
<p>Time required to reach each point using different methods.</p> "> Figure 10
<p>Fire spread area and statistical spread area for each point under dynamic programming with and without probability consideration.</p> "> Figure 11
<p>Fire spread area and statistical spread area for each point under dynamic programming with and without the use of a filter.</p> ">
Abstract
:1. Introduction
2. Related Work
2.1. Forest Fire Prediction
2.1.1. Objectives and Methods of Fire Prediction
2.1.2. Influencing Factors of Forest Fires
2.2. UAV Path Planning for Forest Fire Monitoring
2.2.1. Path Planning Considering Spatial Factors
2.2.2. Path Planning Considering Fire Risk Factors
3. PPP Module
3.1. Design
3.2. Implementation
3.2.1. Establishing the Logistic Regression Model
- True Positive (TP): Number of positive samples correctly predicted as positive.
- True Negative (TN): Number of negative samples correctly predicted as negative.
- False Positive (FP): Number of negative samples incorrectly predicted as positive (Type I error).
- False Negative (FN): Number of positive samples incorrectly predicted as negative (Type II error).
3.2.2. Dynamic Programming Algorithm
4. Experiments and Results
4.1. Experimental Setup
- Group 1 (Conventional Method): Utilizes the DP algorithm, considering only the distances between locations without accounting for fire probabilities.
- Group 2: Utilizes the DP algorithm, considering both distances and fire probabilities without filtering out the low-risk points.
- Group 3: Utilizes the DP algorithm, considering only distances, with the low-risk points filtered out.
- Group 4 (Our Method): Utilizes the DP algorithm, considering both distances and fire probabilities, with the low-risk points filtered out.
4.2. Assessment Method
4.3. Results and Evaluation
4.4. Analysis
5. Discussion and Future Work
5.1. Discussion
5.2. Future Work
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ajin, R.; Ciobotaru, A.-M.; Vinod, P.; Jacob, M.K. Forest and wildland fire risk assessment using geospatial techniques: A case study of Nemmara forest division, Kerala, India. J. Wetl. Biodivers. 2015, 5, 29–37. [Google Scholar]
- Ertugrul, M.; Varol, T.; Ozel, H.B.; Cetin, M.; Sevik, H. Influence of climatic factor of changes in forest fire danger and fire season length in Turkey. Environ. Monit. Assess. 2021, 193, 28. [Google Scholar] [CrossRef] [PubMed]
- Partheepan, S.; Sanati, F.; Hassan, J. Autonomous unmanned aerial vehicles in bushfire management: Challenges and opportunities. Drones 2023, 7, 47. [Google Scholar] [CrossRef]
- Qiao, C.; Wu, L.; Chen, T.; Huang, Q.; Li, Z. Study on forest fire spreading model based on remote sensing and GIS. In Proceedings of the IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2018; p. 022017. [Google Scholar]
- Yuan, C.; Zhang, Y.; Liu, Z. A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can. J. For. Res. 2015, 45, 783–792. [Google Scholar] [CrossRef]
- Ecke, S.; Dempewolf, J.; Frey, J.; Schwaller, A.; Endres, E.; Klemmt, H.-J.; Tiede, D.; Seifert, T. UAV-based forest health monitoring: A systematic review. Remote Sens. 2022, 14, 3205. [Google Scholar] [CrossRef]
- Li, M.; Xu, W.; Xu, K.; Fan, J.; Hou, D. Review of fire detection technologies based on video image. J. Theor. Appl. Inf. Technol. 2013, 49, 700–707. [Google Scholar]
- Yuan, C.; Liu, Z.; Zhang, Y. UAV-based forest fire detection and tracking using image processing techniques. In Proceedings of the 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Denver, CO, USA, 9–12 June 2015; pp. 639–643. [Google Scholar]
- Zhang, L.; Wang, M.; Fu, Y.; Ding, Y. A forest fire recognition method using UAV images based on transfer learning. Forests 2022, 13, 975. [Google Scholar] [CrossRef]
- Xu, N.; Rangwala, S.; Chintalapudi, K.K.; Ganesan, D.; Broad, A.; Govindan, R.; Estrin, D. A wireless sensor network for structural monitoring. In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, MD, USA, 3–5 November 2004; pp. 13–24. [Google Scholar]
- Mao, G.; Fidan, B.; Anderson, B.D. Wireless sensor network localization techniques. Comput. Netw. 2007, 51, 2529–2553. [Google Scholar] [CrossRef]
- Belbachir, A.; Escareno, J.; Rubio, E.; Sossa, H. Preliminary results on UAV-based forest fire localization based on decisional navigation. In Proceedings of the 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), Cancun, Mexico, 23–25 November 2015; pp. 377–382. [Google Scholar]
- Radmanesh, M.; Kumar, M.; Guentert, P.H.; Sarim, M. Overview of path-planning and obstacle avoidance algorithms for UAVs: A comparative study. Unmanned Syst. 2018, 6, 95–118. [Google Scholar] [CrossRef]
- Xu, D.; Qian, H. Research on Path Planning for Relay Drones with Multiple Constraints. In Proceedings of the Wireless Algorithms, Systems, and Applications: 16th International Conference, WASA 2021, Nanjing, China, 25–27 June 2021; Proceedings, Part III 16. pp. 463–470. [Google Scholar]
- Gao, K.; Cao, Z.; Zhang, L.; Chen, Z.; Han, Y.; Pan, Q. A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems. IEEE/CAA J. Autom. Sin. 2019, 6, 904–916. [Google Scholar] [CrossRef]
- Naderpour, M.; Rizeei, H.M.; Ramezani, F. Forest fire risk prediction: A spatial deep neural network-based framework. Remote Sens. 2021, 13, 2513. [Google Scholar] [CrossRef]
- Zhang, L.; Liu, Z.; Zhang, Y.; Ai, J. Intelligent path planning and following for UAVs in forest surveillance and fire fighting missions. In Proceedings of the 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), Xiamen, China, 10–12 August 2018; pp. 1–6. [Google Scholar]
- Haq, M.A. CNN based automated weed detection system using UAV imagery. Comput. Syst. Sci. Eng. 2022, 42, 246491984. [Google Scholar]
- Haq, M.A.; Rahaman, G.; Baral, P.; Ghosh, A. Deep learning based supervised image classification using UAV images for forest areas classification. J. Indian Soc. Remote Sens. 2021, 49, 601–606. [Google Scholar] [CrossRef]
- Chen, Y.; Zhang, Y.; Xin, J.; Yi, Y.; Liu, D.; Liu, H. A UAV-based forest fire detection algorithm using convolutional neural network. In Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China, 25–27 July 2018; pp. 10305–10310. [Google Scholar]
- Huang, J.; Xu, Z.; Yang, F.; Zhang, W.; Cai, S.; Luo, J.; Xie, G.; Li, T. Fire Risk Assessment and Warning Based on Hierarchical Density-Based Spatial Clustering Algorithm and Grey Relational Analysis. Math. Probl. Eng. 2022. [Google Scholar] [CrossRef]
- Chang, Y.; Zhu, Z.; Bu, R.; Chen, H.; Feng, Y.; Li, Y.; Hu, Y.; Wang, Z. Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landsc. Ecol. 2013, 28, 1989–2004. [Google Scholar] [CrossRef]
- Pan, J.; Wang, W.; Li, J. Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China. Nat. Hazards 2016, 81, 1879–1899. [Google Scholar] [CrossRef]
- Ray, T.; Malasiya, D.; Verma, A.; Purswani, E.; Qureshi, A.; Khan, M.L.; Verma, S. Characterization of spatial–temporal distribution of forest fire in Chhattisgarh, India, using MODIS-based active fire data. Sustainability 2023, 15, 7046. [Google Scholar] [CrossRef]
- Carta, F.; Zidda, C.; Putzu, M.; Loru, D.; Anedda, M.; Giusto, D. Advancements in forest fire prevention: A comprehensive survey. Sensors 2023, 23, 6635. [Google Scholar] [CrossRef] [PubMed]
- Casbeer, D.W.; Beard, R.W.; McLain, T.W.; Li, S.-M.; Mehra, R.K. Forest fire monitoring with multiple small UAVs. In Proceedings of the 2005 American Control Conference, Portland, OR, USA, 8–10 June 2005; pp. 3530–3535. [Google Scholar]
- Aydin, B.; Selvi, E.; Tao, J.; Starek, M.J. Use of fire-extinguishing balls for a conceptual system of drone-assisted wildfire fighting. Drones 2019, 3, 17. [Google Scholar] [CrossRef]
- Zhao, L.; Shi, Y.; Liu, B.; Hovis, C.; Duan, Y.; Shi, Z. Finer classification of crops by fusing UAV images and Sentinel-2A data. Remote Sens. 2019, 11, 3012. [Google Scholar] [CrossRef]
- Khan, A.; Gupta, S.; Gupta, S.K. Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. Int. J. Disaster Risk Reduct. 2020, 47, 101642. [Google Scholar] [CrossRef]
- Zhao, Y.; Zheng, Z.; Liu, Y. Survey on computational-intelligence-based UAV path planning. Knowl.-Based Syst. 2018, 158, 54–64. [Google Scholar] [CrossRef]
- Bharany, S.; Sharma, S.; Frnda, J.; Shuaib, M.; Khalid, M.I.; Hussain, S.; Iqbal, J.; Ullah, S.S. Wildfire monitoring based on energy efficient clustering approach for FANETS. Drones 2022, 6, 193. [Google Scholar] [CrossRef]
- Deshmukh, A.A.; Sonar, S.D.; Ingole, R.V.; Agrawal, R.; Dhule, C.; Morris, N.C. Satellite image segmentation for forest fire risk detection using gaussian mixture models. In Proceedings of the 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 4–6 May 2023; pp. 806–811. [Google Scholar]
- Moulianitis, V.; Thanellas, G.; Xanthopoulos, N.; Aspragathos, N.A. Evaluation of UAV based schemes for forest fire monitoring. In Advances in Service and Industrial Robotics, Proceedings of the 27th International Conference on Robotics in Alpe-Adria Danube Region (RAAD 2018), Patras, Greece, 7–8 June 2018; Springer: Berlin/Heidelberg, Germany, 2018; pp. 143–150. [Google Scholar]
- Qadir, Z.; Zafar, M.H.; Moosavi, S.K.R.; Le, K.N.; Mahmud, M.P. Autonomous UAV path-planning optimization using metaheuristic approach for predisaster assessment. IEEE Internet Things J. 2021, 9, 12505–12514. [Google Scholar] [CrossRef]
- Ozkan, O. Optimization of the distance-constrained multi-based multi-UAV routing problem with simulated annealing and local search-based matheuristic to detect forest fires: The case of Turkey. Appl. Soft Comput. 2021, 113, 108015. [Google Scholar] [CrossRef]
- Wang, C.; Liu, P.; Zhang, T.; Sun, J. The adaptive vortex search algorithm of optimal path planning for forest fire rescue UAV. In Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing China, 12–14 October 2018; pp. 400–403. [Google Scholar]
- Zhou, J.; Zhang, W.; Zhang, Y.; Zhao, Y.; Ma, Y. Optimal path planning for UAV patrolling in forest fire prevention. In Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018), Chengdu, China, 16–18 October; 9th ed. Springer: Berlin/Heidelberg, Germany, 2019; pp. 2209–2218. [Google Scholar]
- Machmudah, A.; Shanmugavel, M.; Parman, S.; Manan, T.S.A.; Dutykh, D.; Beddu, S.; Rajabi, A. Flight trajectories optimization of fixed-wing UAV by bank-turn mechanism. Drones 2022, 6, 69. [Google Scholar] [CrossRef]
- Ozkan, O.; Kilic, S. UAV routing by simulation-based optimization approaches for forest fire risk mitigation. Ann. Oper. Res. 2023, 320, 937–973. [Google Scholar] [CrossRef]
- Xu, Y.; Li, J.; Zhang, F. A UAV-based forest fire patrol path planning strategy. Forests 2022, 13, 1952. [Google Scholar] [CrossRef]
- Abedi Gheshlaghi, H.; Feizizadeh, B.; Blaschke, T. GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. J. Environ. Plan. Manag. 2020, 63, 481–499. [Google Scholar] [CrossRef]
- Dreiseitl, S.; Ohno-Machado, L. Logistic regression and artificial neural network classification models: A methodology review. J. Biomed. Inform. 2002, 35, 352–359. [Google Scholar] [CrossRef]
- Abid, F.; Nouma, I. Predicting forest fire in algeria using data mining techniques: Case study of the decision tree algorithm. In Advanced Intelligent Systems for Sustainable Development (AI2SD’2019); Springer: Cham, Switzerland, 2020; Volume 4, pp. 363–370. [Google Scholar]
- Cortez, P.; de Jesus Raimundo Morais, A. A Data Mining Approach to Predict Forest Fires using Meteorological Data. 2007. Available online: https://repositorium.sdum.uminho.pt/handle/1822/8039 (accessed on 10 May 2024).
- Lee, S.-I.; Lee, H.; Abbeel, P.; Ng, A.Y. Efficient L1 regularized logistic regression. In Proceedings of the AAAI, Boston, MA, USA, 16–20 July 2006; pp. 401–408. [Google Scholar]
- Sperandei, S. Understanding logistic regression analysis. Biochem. Medica 2014, 24, 12–18. [Google Scholar] [CrossRef] [PubMed]
- Kc, U.; Aryal, J.; Hilton, J.; Garg, S. A surrogate model for rapidly assessing the size of a wildfire over time. Fire 2021, 4, 20. [Google Scholar] [CrossRef]
- Pavan, D.; Suresh, M. Technology for Power Supply to UAVs through Medium of Air. In Proceedings of the International Conference on Unmanned Aerial System in Geomatics, Roorkee, India, 2–4 April 2021; pp. 571–578. [Google Scholar]
- Song, H.-S.; Lee, S.-H. Effects of wind and tree density on forest fire patterns in a mixed-tree species forest. For. Sci. Technol. 2017, 13, 9–16. [Google Scholar] [CrossRef]
Symbol | Abbreviation | Full Name |
---|---|---|
x1 | T | Temperature |
x2 | RH | Relative humidity |
x3 | R | Rain |
x4 | DMC | Duff Moisture Code |
x5 | ISI | Initial Spread Index |
Model Variable | Coefficient | Standard Error | Wald Test | Significance | Degree of Freedom | Exp(B) |
---|---|---|---|---|---|---|
x1 | 0.3517 | 0.091 | 15.063 | 0.000 | 1 | 1.4215 |
x2 | −0.0168 | 0.096 | 0.031 | 0.861 | 1 | 0.9834 |
x3 | −0.8861 | 0.289 | 9.396 | 0.002 | 1 | 0.4123 |
x4 | −0.0702 | 0.099 | 0.502 | 0.479 | 1 | 0.9322 |
x5 | 0.3286 | 0.118 | 7.768 | 0.005 | 1 | 1.3890 |
constant | 0.0466 | 0.093 | 0.249 | 0.618 | 1 | 1.0477 |
Precision | Recall | F1 Score | Support | |
---|---|---|---|---|
0 | 0.69 | 0.70 | 0.70 | 67 |
1 | 0.76 | 0.76 | 0.76 | 86 |
Macro Avg | 0.73 | 0.73 | 0.73 | 153 |
Weighted Avg | 0.73 | 0.73 | 0.73 | 153 |
Group | DP Algorithm Considers Distance | DP Algorithm Considers Probability | Filters Low-Risk Points |
---|---|---|---|
Group 1 (conventional) | √ | × | × |
Group 2 | √ | √ | × |
Group 3 | √ | × | √ |
Group 4 (our method) | √ | √ | √ |
Point | Coordinate | Fire Probability |
---|---|---|
Point 1 | (4, 5) | 0.06 |
Point 2 | (7, 4) | 0.65 |
Point 3 | (5, 6) | 0.46 |
Point 4 | (5, 4) | 0.01 |
Point 5 | (2, 5) | 0.78 |
Point 6 | (8, 6) | 0.15 |
Point 7 | (4, 3) | 0.76 |
Point 8 | (2, 4) | 0.82 |
Point | Coordinate | Fire Probability |
---|---|---|
Point 1 | (4, 5) | 0.1 |
Point 2 | (7, 4) | 0.7 |
Point 3 | (5, 6) | 0.12 |
Point 4 | (5, 4) | 0.05 |
Point 5 | (2, 5) | 0.8 |
Point 6 | (8, 6) | 0.11 |
Point 7 | (4, 3) | 0.58 |
Point 8 | (2, 4) | 0.82 |
Point Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
Probability | 0.06 | 0.65 | 0.46 | 0.01 | 0.78 | 0.15 | 0.76 | 0.82 |
Normalized Probability | 0.015 | 0.165 | 0.117 | 0.003 | 0.198 | 0.038 | 0.193 | 0.208 |
A(w/o P) | 55.833 | 199.443 | 78.967 | 259.932 | 29.944 | 141.285 | 307.533 | 20 |
A(w/P) | 123.854 | 236.287 | 157.332 | 94.377 | 29.944 | 310.031 | 68.899 | 20 |
SA(w/o P) | 0.85 | 32.903 | 9.22 | 0.66 | 5.928 | 5.379 | 59.321 | 4.162 |
SA(w/P) | 1.886 | 38.981 | 18.369 | 0.24 | 5.928 | 11.803 | 13.29 | 4.162 |
Point Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
Probability | 0.06 | 0.65 | 0.46 | 0.01 | 0.78 | 0.15 | 0.76 | 0.82 |
Normalized probability | 0.015 | 0.165 | 0.117 | 0.003 | 0.198 | 0.038 | 0.193 | 0.208 |
A(w/o P) | 123.854 | 236.287 | 157.332 | 94.377 | 29.944 | 310.031 | 68.899 | 20 |
A(w/P) | 400 | 131.397 | 204.24 | 400 | 29.944 | 400 | 68.899 | 20 |
SA(w/o P) | 1.886 | 38.981 | 18.369 | 0.24 | 5.928 | 11.803 | 13.29 | 4.162 |
SA(w/P) | 6.091 | 21.677 | 23.845 | 1.015 | 5.928 | 15.228 | 13.29 | 4.162 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wang, Y.; Gao, F.; Li, M. Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment. Fire 2024, 7, 254. https://doi.org/10.3390/fire7070254
Wang Y, Gao F, Li M. Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment. Fire. 2024; 7(7):254. https://doi.org/10.3390/fire7070254
Chicago/Turabian StyleWang, Yuqin, Fengsen Gao, and Minghui Li. 2024. "Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment" Fire 7, no. 7: 254. https://doi.org/10.3390/fire7070254