Automatic machine learning Framework for Forest fire forecasting

Abstract

Based on the automatic machine learning framework, combined with the characteristics of forest fire meteorological data and the adaptive requirements of forest fire prediction, this paper optimizes the data preprocessing, parameter learning, loss function and other links of auto-sklearn, builds a forest fire risk prediction framework with regional adaptive characteristics. Based on the forest meteorological fire risk data, a forest fire risk prediction model with regional characteristics and self-learning characteristics is constructed to solve the problems of low compatibility of the existing machine learning methods with binary unbalanced forest fire data, improve the accuracy of forest fire prediction and provide decision-making basis for forestry risk management. The comparative analysis results show that the prediction accuracy of the improved framework in different test sets is improved by 13% on average. Compared with the existing machine learning model for forest fire prediction, the prediction accuracy of the framework proposed in this paper is comprehensively better than the existing methods in terms of real forest fire data.

A Brief Review of Machine Learning Algorithms in Forest Fires Science

“…Ref. [66] investigated the occurrence of fire forecasting by analyzing weather data with the auto-learn framework, achieving an accuracy of 87%.…”

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

“…They claimed that their method could predict the FSS accurately in many regions globally. Table 4 presents some applications of machine learning algorithms in forest fire forecasting using a different type of data collected from weather, environmental, socio-economics, and infrastructure details of the region [132,135,136,[138][139][140][141]144].…”