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Forest fire spread model based on the grey system theory

机译:基于灰色系统理论的森林火灾传播模型

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摘要

Accurate prediction of forest fire spread is very essential for minimizing its effects. Although many models have been developed to predict the forest fire spread, all these models require several parameters, sometimes, cannot be obtained in a real time. In this paper, the grey system theory was applied for forest fire spread model developing. By preprocessing and fusing the MODIS remote sensing data, the sequence data of the grey model can be confirmed. After making precision comparison among least square estimation algorithm, least square interpolation algorithm and ER algorithm, forest fire spread GM(1, 1) model was developed with ER algorithm and precision of the model was evaluated at the same time. The results showed that the fitting precision and predicting precision of the model were both high, of which the one-level model made up 50%, the two-level 25% and the model between the one-level and the two-level 25%. The prediction accuracy of forest fire spreading model was tested to meet the requirement of modeling. GM(1, 1) model provided a new approach for the study of forest fire spread simulation.
机译:精确预测森林火灾传播对于最大限度地减少其效果是至关重要的。虽然已经开发了许多模型来预测森林火灾传播,但所有这些模型需要几个参数,有时候,无法在实时获得。本文采用了灰色系统理论,适用于森林火灾传播模型发展。通过预处理和融合MODIS遥感数据,可以确认灰色模型的序列数据。在对至少方形估计算法中进行精度比较后,最小二乘内插算法和ER算法,森林火灾扩展Gm(1,1)模型是用ER算法开发的,同时评估模型的精度。结果表明,该模型的拟合精度和预测精度都很高,其中单级模型组成50%,两级25%,二级和两级25%之间的模型。测试了森林灭火模型的预测精度,以满足建模要求。 GM(1,1)模型为森林火灾扩展模拟进行了一种新方法。

著录项

  • 来源
    《Journal of supercomputing 》 |2020年第5期| 3602-3614| 共13页
  • 作者单位

    Beijing Forestry Univ Beijing Key Lab Precis Forestry 35 Qinghua East Rd Box 111 Beijing 100083 Peoples R China|Beijing Forestry Univ Coll Forestry 35 Qinghua East Rd Box 111 Beijing 100083 Peoples R China;

    Beijing Forestry Univ Beijing Key Lab Precis Forestry 35 Qinghua East Rd Box 111 Beijing 100083 Peoples R China|Beijing Forestry Univ Coll Forestry 35 Qinghua East Rd Box 111 Beijing 100083 Peoples R China;

    Beijing Forestry Univ Beijing Key Lab Precis Forestry 35 Qinghua East Rd Box 111 Beijing 100083 Peoples R China|Beijing Forestry Univ Coll Forestry 35 Qinghua East Rd Box 111 Beijing 100083 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Least square estimation; Least square estimation interpolation; ER algorithm; MODIS image; GM(1; 1);

    机译:最小二乘估计;最小二乘估计插值;ER算法;MODIS图像;GM(1;1);

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