Modeling method of mountain fire risk prediction based on stacking algorithm

Abstract

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

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for predicting and modeling wildfire risks 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 ​​...

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