Forest Fire Risk Prediction Based On Deep Learning

As one of the important human resources,forest resources not only provide a lot of resources for human production and life,but also maintain the ecological balance of the earth.But at present,the frequent occurrence of forest fires,resulting in a large number of people and property losses,has become one of the most important natural disasters threatening forest resources.Therefore,it is of great practical significance to study the methods of forest fire prediction for avoiding the occurrence of forest fires and protecting forest resources.This paper mainly analyzes the driving factors that affect the occurrence of forest fires,and starts from three aspects: the law of forest fires,the probability of forest fires,and the scale of forest fires.It uses the deep learning algorithm to model and analyze,and finally forms a more comprehensive forest fire prediction method.This paper first introduces the acquisition and processing methods of relevant basic data,which forms the data basis for the subsequent experimental analysis,and analyzes the occurrence law of forest fire in Alberta,Canada from three angles of time dimension,space dimension and meteorological distribution;secondly,two kinds of sample sets of meteorological factors and comprehensive factors are made respectively,using binomial logistic model and random Sen The forest model trains two kinds of sample sets,establishes the forest fire prediction model,through the comparative analysis,the random forest prediction model using the comprehensive factor modeling has the highest accuracy,reaching91.49%;then,this paper proposes the fusion of the fire area and the fire duration as the index to judge the scale of the forest fire,defines the forest fire scale index,and uses B P neural network algorithm,cyclic neural network algorithm(RNN)and long and short-term memory network algorithm(LSTM)are used for modeling and analysis.Experimental comparison shows that LSTM model has the highest accuracy of 90.9% in predicting forest fire scale,followed by RNN model and BP model.Finally,the LSTM model is further analyzed to draw model test set samples and full sample ROC respectively AUC values of the curve and ROC curve are 0.918 and 0.942 respectively,which shows that it is scientific to use LSTM model to predict forest fire scale.