Peatland Forest Fire Prevention Using Wireless Sensor Network Based on Naïve Bayes Classifier
Abstract
Recently, peatland forest fires happened massively and gave bad impact for environment. It is necessary to make efforts to reduce of peatland forest fires, Early Warning System (EWS) is one of the solutions. Here, we propose an EWS to prevent forest fire in peatland by using Wireless Sensor Network (WSN). It uses three significant parameters which are oxygen concentration, soil humidity, and environment temperature. Naïve bayes classifier processes the data parameters and then determines forest fire potential. Unusual measurement of the parameters will trigger the classifier decision. Forest fire potential will be displayed through web services. This EWS helps the authorities to monitor and detect forest fire potential in the peatland, so it can be prevented.
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