Strategy for Fighting Wildfires Using Mean-Shift Algorithm
DOI: 10.23977/acss.2021.050108 | Downloads: 15 | Views: 1040
Author(s)
Wenbo Zhang 1
Affiliation(s)
1 School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang, 150000
Corresponding Author
Wenbo ZhangABSTRACT
To deal with the wildfires in Victoria, we need to arrange the drones reasonably. Taking into account capability, safety, economy and topography, we use Mean-Shift algorithm to determinate the optimal numbers and mix of drones and predict the situation of extreme wildfires in the future. Finally, we determine the optimal number and combination of drones after optimization. According to the size, frequency and locations of wildfires in Victoria in 2019, we use logistic model to estimate the general location of the wildfires. And we use Mean Shift algorithm to find the Optimal number, mix and locations of drones. The result is we need 95 SSA drones, 122 repeater drones and 122 drones with two functions, and the total cost is $3,390,000.
KEYWORDS
Mean-Shift algorithm, logistic model, general locationCITE THIS PAPER
Wenbo Zhang. Strategy for Fighting Wildfires Using Mean-Shift Algorithm. Advances in Computer, Signals and Systems (2021) 5: 55-59. DOI: http://dx.doi.org/10.23977/acss.2021.050108
REFERENCES
[1] Wang Mingyu, Shu Lifu, Tian Xiao. A Method for Predicting the Daily Occurrence Probability of Forest Wildfires: China, CN201410057152.5 [P]. 2014-06-25.
[2] Wu Dehui. Dynamic exponential smoothing prediction method and its application [J]. Journal of Systems &Management, 2008, 17(002): 151-155.
[3] Zhang Wenzheng, Zhang Chuanlin, Fu Wenchao. Wireless Sensor Deployment Based on Probability Coverage Model [J]. Journal of Hainan University (Natural Science Edition), 2010, 28(003): 248-251.
Downloads: | 11330 |
---|---|
Visits: | 240225 |
Sponsors, Associates, and Links
-
Power Systems Computation
-
Internet of Things (IoT) and Engineering Applications
-
Computing, Performance and Communication Systems
-
Journal of Artificial Intelligence Practice
-
Journal of Network Computing and Applications
-
Journal of Web Systems and Applications
-
Journal of Electrotechnology, Electrical Engineering and Management
-
Journal of Wireless Sensors and Sensor Networks
-
Journal of Image Processing Theory and Applications
-
Mobile Computing and Networking
-
Vehicle Power and Propulsion
-
Frontiers in Computer Vision and Pattern Recognition
-
Knowledge Discovery and Data Mining Letters
-
Big Data Analysis and Cloud Computing
-
Electrical Insulation and Dielectrics
-
Crypto and Information Security
-
Journal of Neural Information Processing
-
Collaborative and Social Computing
-
International Journal of Network and Communication Technology
-
File and Storage Technologies
-
Frontiers in Genetic and Evolutionary Computation
-
Optical Network Design and Modeling
-
Journal of Virtual Reality and Artificial Intelligence
-
Natural Language Processing and Speech Recognition
-
Journal of High-Voltage
-
Programming Languages and Operating Systems
-
Visual Communications and Image Processing
-
Journal of Systems Analysis and Integration
-
Knowledge Representation and Automated Reasoning
-
Review of Information Display Techniques
-
Data and Knowledge Engineering
-
Journal of Database Systems
-
Journal of Cluster and Grid Computing
-
Cloud and Service-Oriented Computing
-
Journal of Networking, Architecture and Storage
-
Journal of Software Engineering and Metrics
-
Visualization Techniques
-
Journal of Parallel and Distributed Processing
-
Journal of Modeling, Analysis and Simulation
-
Journal of Privacy, Trust and Security
-
Journal of Cognitive Informatics and Cognitive Computing
-
Lecture Notes on Wireless Networks and Communications
-
International Journal of Computer and Communications Security
-
Journal of Multimedia Techniques
-
Automation and Machine Learning
-
Computational Linguistics Letters
-
Journal of Computer Architecture and Design
-
Journal of Ubiquitous and Future Networks