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Forest fire early warning model and system based on deep learning technology

A forest fire and early warning model technology, applied in neural learning methods, biological neural network models, predictions, etc., can solve the problems of lack of forest fire early warning models and systems, and achieve the effect of improving accuracy and foresight

Pending Publication Date: 2020-09-04
CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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Problems solved by technology

[0004] The purpose of the present invention is to provide a forest fire early warning model and system based on deep learning technology, to solve the technical problem of lack of forest fire early warning model and system based on deep learning technology in the prior art

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  • Forest fire early warning model and system based on deep learning technology

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Embodiment

[0044] 1. Introduction to the experimental area

[0045] The experimental area is located in the upper reaches of Dongting Lake, at the junction of western Hunan Province and eastern Guizhou (mainly composed of Qiandongnan Miao and Dong Autonomous Prefecture in Guizhou, a small part of Tongren area, and a small part of Huaihua City in Hunan Province), at the transition from the Yunnan-Guizhou Plateau to the Hunan-Guizhou Hilly Basin zone. as attached figure 2As shown, the geographic location is from 107°11' east longitude to 109°32' east longitude, and from 25°59' to 27°28' north latitude, covering an area of ​​about 37,000 square kilometers. The terrain is complex, mainly plateaus and mountains, with a small amount of hills, karsts and depressions, with great fluctuations. It is the transition zone between the Yunnan-Guizhou Plateau and the Hunan-Guizhou Hilly Basin. The lowest altitude in the area is 137 meters, the highest altitude is 2572 meters, and most areas are betw...

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Abstract

The invention provides a forest fire early warning model. The architecture comprises an adjacent component, a periodic component and an external component, the construction process comprises the steps: S1, converting the air temperature, humidity, wind speed and precipitation of preprocessed meteorological data in each time intervalinto a four-channel approximate image matrix, and then dividing atime axis into two segments which represent the relation of proximity and period similarity of all positions in a time flow; s2, respectively inputting the image matrix in each time slice into an adjacent component and a periodic component for modeling; s3, endowing results of different components with different weights through the parameter matrix, and fusing output results of the adjacent component and the periodic component into a residual component XRes; and S4, mapping an XRes and XExt integration result to [-1, 1] through a Tanh function. According to the invention, a neural network structure with a deep residual network as a core is used to construct a forest fire early warning model.

Description

technical field [0001] The invention belongs to the technical field of forestry information engineering, and in particular relates to a forest fire early warning model and system based on deep learning technology. Background technique [0002] As an important part of the terrestrial ecosystem, forests play a very important role in regulating the ecological balance of the earth. Forest resources not only play an important role in maintaining the balance of the ecosystem, but also bring economic benefits to people. The occurrence of forest fires has had a major impact on people's production and life, and has also brought a huge impact on forestry development and protection. Forest fire prevention is essentially a process of risk management, a management activity aimed at minimizing forest fire accidents and forest fire losses. Therefore, it is the starting point of forest fire prevention management to judge the degree of forest fire risk in advance and improve the awareness ...

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06Q10/04G06Q50/26
CPCG06F30/27G06N3/084G06N3/08G06Q10/04G06Q50/26G06N3/045
Inventor 张贵阙华斐吴鑫杨志高
Owner CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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