Terrain-influenced incremental watchtower expansion for wildfire detection.

Area of Science:

• Environmental Science
• Computer Science
• Operations Research

Background:

• Optimizing early wildfire detection systems is crucial.
• Watchtower systems combined with modern tech are practical but expansion is challenging due to constraints.
• Existing methods re-solve the full optimization problem repeatedly for incremental expansion.

Purpose of the Study:

• To propose a novel approach for the incremental expansion of wildfire detection systems.
• To treat the expansion problem as a submodular set-function maximization problem.
• To develop models and algorithms for staged system expansion under practical constraints.

Main Methods:

• Theoretically proving the wildfire detection system expansion is a submodular set-function maximization problem.
• Developing four distinct models and algorithms for various incremental expansion scenarios.
• Integrating visibility analysis and location allocation for coverage optimization.

Main Results:

• The proposed submodular optimization approach effectively addresses incremental system expansion.
• Algorithms provide excellent coverage while adhering to strict budgetary requirements.
• Demonstrated effectiveness on a practical dataset from NanJing forest park, China.

Conclusions:

• The submodular set-function maximization framework offers an effective alternative to traditional methods for wildfire detection system expansion.
• The developed models and algorithms are practical and adaptable for real-world applications globally.
• This approach facilitates cost-effective and efficient enhancement of early wildfire detection capabilities.