Dear researcher,
Currently I am working with my students on a topic of early detection of forest fires using a network of wireless sensors. I have a problem that I have to solve by artificial intelligence. I have a system ( node) based on a microprocessor with 7 sensors (Temperature, Humidity, CO, CO2, PM2.5 particle detector, wind speed and wind direction) The objective is to determine using these values a percentage of the existence of fires. To do this, I started by learning my system. We put a fire and measure the 7 values while there is a fire or there is not. I want to ask if you can offer me a track, method or algorithm for the supervision of forest fires to calculate a percentage that the fire would take place. Because I am a little confused by all the methods of artificial intelligence: deep learning or other? what functions used? how many hidden layers?
I feel you and your team have identified the optimum values for the different parameters to meet the ignition condition. I feel you can use Hybrid Network model with Long Short Term Memory (LSTM).
Please read applications of LSTM, hope that will work in your case as you will have plenty of data.
If required please communicate with me at [email protected]
Referring to articles with titles "forest fire susceptibility mapping" or "wildfire probability mapping" might be helpful. In such cases you train a network for two conditions: occurrence (valued by 1) and non-occurrence (valued by 0) of fire. Then the network can predict the fire probability for a given condition.
Common intelligent tools for such works are MLP neural networks as well as ANFIS.
Please check this application of ANFIS , I am sure this approach will help you.
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