Predicting forest fire occurrence and incremental fire rate using SAS® 9.4 and SAS® Enterprise Miner
نویسندگان
چکیده
Fast detection of forest fires is a great concern among environmental experts and national park managers because forest fires create economic and ecological damages and endanger human lives. For effective fire control and resource preparation, it is necessary to predict fire occurrences in advance and estimate the possible losses caused by fires. For this purpose, real-time sensor data of weather conditions and fire occurrence are highly recommended to use in order to support the predicting mechanism. The objective of this study is to use SAS® 9.4 and SAS® Enterprise MinerTM 14.1 to predict the probability of fires and to figure out special weather conditions resulting in incremental burned areas in Montesinho Park forest (Portugal). The data set was obtained from the Center for Machine Learning and Intelligent Systems at University of California, Irvine and contains 517 observations and 13 variables from January 2000 to December 2003. Support Vector Machine analyses with variable selection were performed on this data set for fire occurrence prediction with a validation accuracy of approximately 60%. The study also incorporates Incremental Response technique and Hypothesis testing to estimate the increased probability of fire as well as the extra burned area under various conditions. For example, when there is no rain, a 27% higher chance of fires and 4.8 hectares of extra burned area are recorded, compared to when there is rain.
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