Hill fire risk prediction method based on stacking algorithm

Abstract

The invention discloses a hill fire risk prediction method based on a stacking algorithm, which can improve prediction efficiency and prediction accuracy. The hill fire risk prediction method adopts combustible factor data, geographic data, meteorological data, historical hill fire data and other time-space data to predict the hill fire risk. The processing technology of multi-source, heterogeneous, massive time-space data is designed, and the abundant hill fire occurrence prediction feature set is formed. The hill fire risk prediction method has the ability to deal with massive spatio-temporal data; the data-driven modeling is realized to avoid tedious and complex Bayesian modeling process, and the efficiency of spatio-temporal data modeling is improved. At the same time, the hill fire risk prediction method takes into account the characteristics of time, space, dynamic and static characteristics, and realizes the secondary processing generation of the characteristics through the stacking method, which improves the overall effect of hill fire risk prediction. The experimental results show that the AUC index reaches 0.85. The hill fire risk prediction method is suitable for popularizing and applying in the field of data processing technology.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for predicting wildfire risk based on a stacking algorithm. Background technique [0002] Since the 1920s, people have never stopped studying the prediction and early warning of wildfire risk. Thanks to the collection of massive spatio-temporal data such as remote sensing and meteorology, as well as the great progress of modern information processing and analysis capabilities, wildfire risk prediction has relied on technologies such as experiments and numerical calculations in the early days, and now uses various technologies such as data mining and machine learning. rapidly evolving situation. [0003] In the methods of forest fire risk prediction using supervised data mining methods, supervised learning techniques such as Bayesian networks, decision trees, and SVMs are represented. The main method is to use whether a mountain fire (or area of ​​fire) will occ...

AI Technical Summary

Problems solved by technology

At present, spatio-temporal data mining technology has been used in crime and disease prediction analysis, but there is no public related literature on wildfire prediction

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