Here are my comments for the papers I read this week. Forest fires spread modeling using cellular automata approach.
They described a method using cellular automata to simulate how fire spread over an area of island Brac in Croatia. The paper had a great overview of Classification of forest fire models, explanation of cellular automata, well-known Neighborhood Templates, and how Landscapes can be represented as cellular automata. They mentioned that only vegetation characteristics and wind conditions were taken into account as input parameters. I might include more input data if I use this model. Computer vision system for fire detection and report using UAVs Special Issue for Submission.
The main concerns of the paper was how to detect fire using computer vision techniques as well as hardware systems. The paper serves as an explanation to their system rather than how their system is compared to other fire detection models. I might use this pa- per for my research if I want to establish a communication system later on. Using cellular automata to simulate wildfire propagation and to assist in fire management.
Unlike the cellular automaton mentioned in the other two papers, this one did not take into consideration the state of stress of vegetation and the meteorological condition. If it possible, I would like to develop a system that can output different simulations based on different cellular automata models based the ones in this paper and in the other two mentioned above. An FPGA processor for modeling wildfire spreading.
The model was designed to not require too much computational resources and computational power so that it could describe fire behavior in real time. I might use this model if I want to design my simulation model in real time. A Cellular Automata model for fire spreading prediction.
The result was a model of cells that evolve with given transition rules. This model forms the basic foundation my research. I can implement a similar model with these transition rules. Forest fire spread simulating model using cellular automaton with extreme learning machine Extracting Traffic Events and Human Mobility Patterns in Geosocial Media Data for Assessing Real-time Road Traffic View project Understanding human activity pattern.
They mentioned that the accuracy of this model was between 58.45 and 82.08%. I do not think a simulation accuracy of 58.45% is a reliable. This research also used cellular automaton to pre- dict fire propagation, which is similar to the paper ”Forest fire spread simulation algorithm based on cellular automata.”