Prediction and data mining of burned areas of forest fires: Optimized data matching and mining algorithm provides valuable insight

Abstract

Abstract An optimized data-matching machine learning algorithm is developed to provide high-prediction accuracy of total burned areas for specific wildfire incidents. It is applied to a well-studied forest-fire dataset from Portugal Montesinho Natural Park considering 13 input variables. The total burned area distribution of the 517 burn events in that dataset is highly positively skewed. The model is transparent and avoids regressions and hidden layers. This increases its detailed data mining capabilities. It matches the highest burned-area prediction accuracy achieved for this dataset with a wide range of traditional machine learning algorithms. The two-stage prediction process provides informative feature selection that establishes the relative influences of the input variables on burned-area predictions. Optimizing with mean absolute error (MAE) and root mean square error (RMSE) as separate objective functions provides complementary information with which to data mine each total burned-area incident. Such insight offers potential agricultural, ecological, environmental and forestry benefits by improving the understanding of the key influences associated with each burn event. Data mining the differential trends of cumulative absolute error and squared error also provides detailed insight with which to determine the suitability of each optimized solution to accurately predict burned-areas events of specific types. Such prediction accuracy and insight leads to confidence in how each prediction is derived. It provides knowledge to make appropriate responses and mitigate specific burn incidents, as they occur. Such informed responses should lead to short-term and long-term multi-faceted benefits by helping to prevent certain types of burn incidents being repeated or spread.