Random forest-based tunnel operation state sensing model building method

A technology of random forest and operational status, applied in computer parts, character and pattern recognition, instruments, etc., to achieve automatic recognition and improve the level of research and judgment

Inactive Publication Date: 2018-01-09
CHANGAN UNIV
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Problems solved by technology

[0005] However, at present, there is no model that can better adapt to the changing requirements of tunnel operation status and can provide accurate real-time perception a

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  • Random forest-based tunnel operation state sensing model building method
  • Random forest-based tunnel operation state sensing model building method
  • Random forest-based tunnel operation state sensing model building method

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[0034] In order to make the objectives, technical solutions, and advantages of the present invention clearer and clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

[0035] Each decision tree in the random forest is a binary tree, and its generation follows the top-down recursive split principle, that is, the training set is divided sequentially from the root node. In a binary tree, the root node contains all the training set data. According to the principle of minimum node impurity, it is split into a left node and a right node, each containing a subset of the training data. The node continues to split according to the same rule until the branch stop rule is satisfied. Stop growing.

[0036] See figure 2 , A method for establishing a tunnel op...

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Abstract

The invention discloses a random forest-based tunnel operation state sensing model building method. The method comprises the steps of randomly selecting ntree new self-service sample sets, constructing ntree decision trees, randomly selecting mtry features at each node of the decision tree, selecting out one feature to perform branch growth to obtain unbiased estimation of a random forest generalization error, and calculating program running time; and iteratively running all ntree and mtry parameter combinations, outputting unbiased estimation and running time corresponding to all the parameter combinations, determining an optimal ntree and mtry parameter combination value in a random forest, and building a tunnel operation state sensing model. According to the method, a capability of analyzing complex related relationship data can be improved and an over-fitting phenomenon does not easily occur; an actual prediction result shows that the average sensing precision, the recall rate andthe F measure are all superior to those of a comparison model; tunnel operation state change requirements can be better met; and accurate real-time sensing and prediction can be provided for tunnel operation states.

Description

technical field [0001] The invention relates to the field of tunnel engineering, in particular to a method for establishing a random forest-based tunnel operation state perception model. Background technique [0002] In recent years, a large number of extra-long highway tunnels have been built and put into operation, and the highway tunnel has gradually shifted from the peak period of construction to the peak period of operation. However, due to the influence of traffic composition and traffic volume, pollutants in the tunnel continue to accumulate, and it is relatively difficult to discharge or dilute them, making ventilation the primary problem during operation and bringing difficulties to tunnel operation and management. Therefore, it is necessary to analyze the traffic flow data and environmental monitoring data in the tunnel, and formulate corresponding operation control measures after determining the operation status of the tunnel. Among them, the rationality and scie...

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Application Information

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IPC IPC(8): G06K9/62G06Q10/06G06Q50/26
Inventor 陈建勋钱超罗彦斌张馨予李伟吉祥
Owner CHANGAN UNIV
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