date: 2022-12-19T13:32:04Z pdf:PDFVersion: 1.7 pdf:docinfo:title: Research Progress of Forest Fires Spread Trend Forecasting in Heilongjiang Province xmp:CreatorTool: LaTeX with hyperref access_permission:can_print_degraded: true subject: In order to further grasp the scientific method of forecasting the spreading trend of forest fires in Heilongjiang Province, which is located in Northeast China, the basic concepts of forest fires, a geographical overview of Heilongjiang Province, and an overview of forest fire forecasting are mainly introduced. The calculation and computer simulation of various forest fire spread models are reviewed, and the selected model for forest fires spread in Heilongjiang Province is mainly summarized. The research shows that the Wang Zhengfei?Mao Xianmin model has higher accuracy and is more suitable for the actual situation of Heilongjiang Province. However, few studies over the past three decades have updated the formula. Therefore, this empirical model is mainly analyzed in this paper. The nonlinear least squares method is used to re-fit the wind speed correction coefficient, which gets closer results to the actual values, and the Wang Zhengfei?Mao Xianmin model is rewritten and evaluated for a more precise formula. In addition, a brief overview of the commonly used Rothermel mathematical?physical model and the improved ellipse mathematical model is given, which provides a basis for the improvement of the forest fires spread model in Heilongjiang Province. dc:format: application/pdf; version=1.7 pdf:docinfo:creator_tool: LaTeX with hyperref access_permission:fill_in_form: true pdf:encrypted: false dc:title: Research Progress of Forest Fires Spread Trend Forecasting in Heilongjiang Province modified: 2022-12-19T13:32:04Z cp:subject: In order to further grasp the scientific method of forecasting the spreading trend of forest fires in Heilongjiang Province, which is located in Northeast China, the basic concepts of forest fires, a geographical overview of Heilongjiang Province, and an overview of forest fire forecasting are mainly introduced. The calculation and computer simulation of various forest fire spread models are reviewed, and the selected model for forest fires spread in Heilongjiang Province is mainly summarized. The research shows that the Wang Zhengfei?Mao Xianmin model has higher accuracy and is more suitable for the actual situation of Heilongjiang Province. However, few studies over the past three decades have updated the formula. Therefore, this empirical model is mainly analyzed in this paper. The nonlinear least squares method is used to re-fit the wind speed correction coefficient, which gets closer results to the actual values, and the Wang Zhengfei?Mao Xianmin model is rewritten and evaluated for a more precise formula. In addition, a brief overview of the commonly used Rothermel mathematical?physical model and the improved ellipse mathematical model is given, which provides a basis for the improvement of the forest fires spread model in Heilongjiang Province. pdf:docinfo:subject: In order to further grasp the scientific method of forecasting the spreading trend of forest fires in Heilongjiang Province, which is located in Northeast China, the basic concepts of forest fires, a geographical overview of Heilongjiang Province, and an overview of forest fire forecasting are mainly introduced. The calculation and computer simulation of various forest fire spread models are reviewed, and the selected model for forest fires spread in Heilongjiang Province is mainly summarized. The research shows that the Wang Zhengfei?Mao Xianmin model has higher accuracy and is more suitable for the actual situation of Heilongjiang Province. However, few studies over the past three decades have updated the formula. Therefore, this empirical model is mainly analyzed in this paper. The nonlinear least squares method is used to re-fit the wind speed correction coefficient, which gets closer results to the actual values, and the Wang Zhengfei?Mao Xianmin model is rewritten and evaluated for a more precise formula. In addition, a brief overview of the commonly used Rothermel mathematical?physical model and the improved ellipse mathematical model is given, which provides a basis for the improvement of the forest fires spread model in Heilongjiang Province. pdf:docinfo:creator: Xiaoxue Wang, Chengwei Wang, Guangna Zhao, Hairu Ding and Min Yu meta:author: Xiaoxue Wang, Chengwei Wang, Guangna Zhao, Hairu Ding and Min Yu meta:creation-date: 2022-12-19T13:32:04Z created: 2022-12-19T13:32:04Z access_permission:extract_for_accessibility: true Creation-Date: 2022-12-19T13:32:04Z Author: Xiaoxue Wang, Chengwei Wang, Guangna Zhao, Hairu Ding and Min Yu producer: GPL Ghostscript 9.50 pdf:docinfo:producer: GPL Ghostscript 9.50 pdf:unmappedUnicodeCharsPerPage: 0 dc:description: In order to further grasp the scientific method of forecasting the spreading trend of forest fires in Heilongjiang Province, which is located in Northeast China, the basic concepts of forest fires, a geographical overview of Heilongjiang Province, and an overview of forest fire forecasting are mainly introduced. The calculation and computer simulation of various forest fire spread models are reviewed, and the selected model for forest fires spread in Heilongjiang Province is mainly summarized. The research shows that the Wang Zhengfei?Mao Xianmin model has higher accuracy and is more suitable for the actual situation of Heilongjiang Province. However, few studies over the past three decades have updated the formula. Therefore, this empirical model is mainly analyzed in this paper. The nonlinear least squares method is used to re-fit the wind speed correction coefficient, which gets closer results to the actual values, and the Wang Zhengfei?Mao Xianmin model is rewritten and evaluated for a more precise formula. In addition, a brief overview of the commonly used Rothermel mathematical?physical model and the improved ellipse mathematical model is given, which provides a basis for the improvement of the forest fires spread model in Heilongjiang Province. Keywords: forest fires spread; fire behavior forecast; empirical model access_permission:modify_annotations: true dc:creator: Xiaoxue Wang, Chengwei Wang, Guangna Zhao, Hairu Ding and Min Yu description: In order to further grasp the scientific method of forecasting the spreading trend of forest fires in Heilongjiang Province, which is located in Northeast China, the basic concepts of forest fires, a geographical overview of Heilongjiang Province, and an overview of forest fire forecasting are mainly introduced. The calculation and computer simulation of various forest fire spread models are reviewed, and the selected model for forest fires spread in Heilongjiang Province is mainly summarized. The research shows that the Wang Zhengfei?Mao Xianmin model has higher accuracy and is more suitable for the actual situation of Heilongjiang Province. However, few studies over the past three decades have updated the formula. Therefore, this empirical model is mainly analyzed in this paper. The nonlinear least squares method is used to re-fit the wind speed correction coefficient, which gets closer results to the actual values, and the Wang Zhengfei?Mao Xianmin model is rewritten and evaluated for a more precise formula. In addition, a brief overview of the commonly used Rothermel mathematical?physical model and the improved ellipse mathematical model is given, which provides a basis for the improvement of the forest fires spread model in Heilongjiang Province. dcterms:created: 2022-12-19T13:32:04Z Last-Modified: 2022-12-19T13:32:04Z dcterms:modified: 2022-12-19T13:32:04Z title: Research Progress of Forest Fires Spread Trend Forecasting in Heilongjiang Province xmpMM:DocumentID: uuid:133ead47-b7be-11f8-0000-1a1cdac57c6f Last-Save-Date: 2022-12-19T13:32:04Z pdf:docinfo:keywords: forest fires spread; fire behavior forecast; empirical model pdf:docinfo:modified: 2022-12-19T13:32:04Z meta:save-date: 2022-12-19T13:32:04Z Content-Type: application/pdf X-Parsed-By: org.apache.tika.parser.DefaultParser creator: Xiaoxue Wang, Chengwei Wang, Guangna Zhao, Hairu Ding and Min Yu dc:subject: forest fires spread; fire behavior forecast; empirical model access_permission:assemble_document: true xmpTPg:NPages: 14 pdf:charsPerPage: 351 access_permission:extract_content: true access_permission:can_print: true meta:keyword: forest fires spread; fire behavior forecast; empirical model access_permission:can_modify: true pdf:docinfo:created: 2022-12-19T13:32:04Z