CN110969303B - Tree height prediction method based on rational Charles model - Google Patents
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Abstract
The invention discloses a tree height prediction method based on a rational Charles model, which comprises the following steps: collecting average tree height-age data of a tree range of a height to be predicted, and determining uncertain parameters in the rational model according to the average tree height-age data to obtain a universal rational model; carrying out typical correlation analysis on the tree height and the site factors and the meteorological factors respectively to obtain the site factors and the meteorological factors related to the tree height; then, obtaining a functional relation of meteorological factors affecting the tree height along with the age of the tree to obtain an influence function; according to a genetic algorithm, an influence function is added into a universal rational Charles model in a fitting way, the weights of meteorological factors and site factors related to tree heights are determined, a tree height growth model is obtained, and a tree to be predicted is highly predicted, so that a prediction result is obtained. The invention can accurately predict the height of trees in different growth environments, so that maintainers have better references for making a felling maintenance plan.
Description
Technical Field
The invention belongs to the field of tree height prediction, and particularly relates to a tree height prediction method based on a rational Charles model.
Background
In recent years, forestry in China rapidly develops, the implementation of national returning to forest policies and the improvement of environmental protection requirements of people have been very prominent, and the threat of trees for safe operation of transmission lines is very prominent. This situation is severe whether a newly built line or a line is put into production. In order to effectively prevent the problem, a reasonable and efficient maintenance plan is formulated, and the prediction of the growth height of the tree is necessary. In the prior art, the Lechad model is used as a classical growth model, has high universality, can simulate the heights of various trees, and has fewer operation steps compared with a common least square fitting formula. However, the richard model only has an age-tree height relationship, which makes the richard model lack of adaptability to the environment in practical application, resulting in low accuracy of tree height prediction, and the practical application value needs to be improved.
Disclosure of Invention
Aiming at the defects in the prior art, the tree height prediction method based on the rational Charles model solves the problem that the accuracy of the tree height prediction in the prior art is not high.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a tree height prediction method based on a rational Charles model comprises the following steps:
s1, collecting average tree height-age data of a tree range of a height to be predicted, and determining uncertain parameters in a rational model according to the average tree height-age data to obtain a general rational model;
s2, obtaining the tree heights of all tree age stages through a general rational model, and respectively carrying out typical correlation analysis on the tree heights and the site factors and the meteorological factors to obtain the site factors and the meteorological factors related to the tree heights;
s3, according to meteorological factors related to the tree height, acquiring a functional relation of the meteorological factors affecting the tree height along with the age of the tree, and obtaining an affecting function;
s4, fitting and adding an influence function into the general rational Charles model according to a genetic algorithm, and determining the weights of meteorological factors and site factors related to the tree height to obtain a tree height growth model;
s5, according to the tree height growth model, carrying out high prediction on the tree to be predicted, and obtaining a prediction result.
Further, the uncertain parameters in the step S1 are fitted through maximum likelihood estimation, and the specific method is as follows:
a1, according to tree height samples in the range of the tree with the height to be predicted, arranging the tree height samples according to the tree ages to obtain a sample set D= { x 1 ,x 2 ,...,x n }；
A2, acquiring joint probability density l (theta) according to the sample set D as follows:
a3, according to gradient operatorObtaining maximum likelihood value theta max The solution equation of (2) is:
wherein θ= [ θ ] 1 ,θ 2 ,...,θ z ,...,θ M ]θ represents an uncertain parameter set, θ z Represents an uncertainty parameter, z=1, 2,..m, M represents the total number of uncertainty parameters,x i represents tree height samples, i=1, 2,..n, n represents the total number of tree height samples, p (·) represents the likelihood function.
Further, the step S2 includes the following sub-steps:
s2.1, obtaining the tree heights of all tree age stages through a universal rational model;
s2.2, performing typical correlation analysis on meteorological factors and site factors corresponding to the tree heights at each tree age stage through an drift diameter coefficient analysis method to obtain the site factors and meteorological factors related to the tree heights.
Further, in the step S2.2, the specific method for performing typical correlation analysis on the meteorological factors and the site factors corresponding to the tree heights at each tree age stage by using the drift diameter coefficient analysis method is as follows: and according to the drift diameter coefficient analysis method, carrying out drift diameter analysis by taking the tree height of each tree age stage as a dependent variable, obtaining the drift diameter factors corresponding to each meteorological factor and each site factor, judging whether the drift diameter factors are larger than 1, if so, correlating the corresponding meteorological factors or site factors with the tree height, and otherwise, correlating not.
Further, the influence function b in the step S3 j The method comprises the following steps:
b j ＝k ct
wherein k and c represent coefficients to be solved for the influence function, b j Representing meteorological factor g j As a function of the influence of the age of the tree, j=1, 2,..s, s, represents the total number of meteorological factors associated with the height of the tree, and t represents time.
Further, the step S4 includes the following sub-steps:
s4.1, setting a parameter space, and randomly generating N solutions to obtain an initial solution set;
s4.2, acquiring initial weights of the meteorological factors and initial weights of the site factors through an initial solution set, taking the initial weights of the meteorological factors and the initial weights of the site factors as individuals, and fitting a function formula y through a genetic algorithm according to the initial weights;
s4.3, calculating fitness of individuals, selecting N individuals from the current population according to fitness proportion, and taking the N individuals as N parent individuals;
s4.4, carrying out random pairing on parent individuals, carrying out two-point crossover operation with larger crossover probability, and carrying out mutation operation on crossed individuals with smaller probability to obtain a plurality of new generation individuals and new generation groups, wherein the new generation individuals are updated meteorological factor weights or site factor weights, and the new generation groups are a collection of the new generation individuals;
s4.5, judging whether a termination condition is met, if yes, terminating to obtain the weight of the meteorological factors and the weight of the site factors, otherwise, returning to the step S4.3;
s4.6, obtaining a final function formula y according to the weight of the meteorological factors and the weight of the site factors, and obtaining the tree height growth model.
Further, in the step S4.2, the function formula y is:
y＝x 0 +a 1 ln b 1 g 1 +a 2 ln b 2 g 2 +,...,+a d ln b j g j +,...,+a s ln b s g s +a s+1 ln q 1 +a s+2 ln q 2 +，...，+a s+m ln q m
wherein y represents the predicted value of the current tree height, and x 0 Representing the height of the fitting tree, g j Represents the meteorological factors associated with the tree height, j=1, 2,..s, s represents the total number of meteorological factors associated with the tree height, b j Representing meteorological factor g j An influence function with age of tree, a d Representing the calculated coefficients obtained by the initial solution set, d=1, 2,..s,..s+m, { q 1 ,q 2 ,...,q m And m represents the total number of the floor factors related to the tree height.
Further, the termination condition in the step S4.5 is: the absolute value of the difference between the predicted value and the true value of the tree height is less than 0.1m.
Further, the step S5 includes the steps of:
s5.1, collecting the age of a tree to be predicted, and obtaining meteorological factors and site factors of years corresponding to the age;
s5.2, inputting the meteorological factors and the site factors into a tree height growth model to obtain a tree height prediction result.
The beneficial effects of the invention are as follows:
(1) The tree height growth model is constructed based on the rational Charles model, and has better adaptability and better prediction effect for different weather and site factors.
(2) The invention can accurately predict the height of trees in different growth environments, so that maintainers have better references for making a felling maintenance plan.
Drawings
Fig. 1 is a flowchart of a tree height prediction method based on a rational charles model.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
Embodiments of the present invention are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a tree height prediction method based on a rational charles model includes the following steps:
s1, collecting average tree height-age data of a tree range of a height to be predicted, and determining uncertain parameters in a rational model according to the average tree height-age data to obtain a general rational model;
s2, obtaining the tree heights of all tree age stages through a general rational model, and respectively carrying out typical correlation analysis on the tree heights and the site factors and the meteorological factors to obtain the site factors and the meteorological factors related to the tree heights;
s3, according to meteorological factors related to the tree height, acquiring a functional relation of the meteorological factors affecting the tree height along with the age of the tree, and obtaining an affecting function;
s4, fitting and adding an influence function into the general rational Charles model according to a genetic algorithm, and determining the weights of meteorological factors and site factors related to the tree height to obtain a tree height growth model;
s5, according to the tree height growth model, carrying out high prediction on the tree to be predicted, and obtaining a prediction result.
In the step S1, the uncertain parameters are fitted through maximum likelihood estimation, and the specific method comprises the following steps:
a1, according to tree height samples in the range of the tree with the height to be predicted, arranging the tree height samples according to the tree ages to obtain a sample set D= { x 1 ,x 2 ,...,x n }；
A2, acquiring joint probability density l (theta) according to the sample set D as follows:
a3, according to gradient operatorObtaining maximum likelihood value theta max The solution equation of (2) is:
wherein θ= [ θ ] 1 ,θ 2 ,...,θ z ,...,θ M ]θ represents an uncertain parameter set, θ z Represents an uncertainty parameter, z=1, 2,..m, M represents the total number of uncertainty parameters,x i represents tree height samples, i=1, 2,..n, n represents the total number of tree height samples, p (·) represents the likelihood function.
Step S2 comprises the following sub-steps:
s2.1, obtaining the tree heights of all tree age stages through a universal rational model;
s2.2, performing typical correlation analysis on meteorological factors and site factors corresponding to the tree heights at each tree age stage through an drift diameter coefficient analysis method to obtain the site factors and meteorological factors related to the tree heights.
In the step S2.2, the specific method for carrying out typical correlation analysis on the meteorological factors and the site factors corresponding to the tree heights of the tree age stages by using the drift diameter coefficient analysis method comprises the following steps: and according to the drift diameter coefficient analysis method, carrying out drift diameter analysis by taking the tree height of each tree age stage as a dependent variable, obtaining the drift diameter factors corresponding to each meteorological factor and each site factor, judging whether the drift diameter factors are larger than 1, if so, correlating the corresponding meteorological factors or site factors with the tree height, and otherwise, correlating not.
Influence function b in step S3 j The method comprises the following steps:
b j ＝k ct
wherein k and c represent coefficients to be solved for the influence function, b j Representing meteorological factor g j As a function of the influence of the age of the tree, j=1, 2,..s, s, represents the total number of meteorological factors associated with the height of the tree, and t represents time.
Step S4 comprises the following sub-steps:
s4.1, setting a parameter space, and randomly generating N solutions to obtain an initial solution set;
s4.2, acquiring initial weights of the meteorological factors and initial weights of the site factors through an initial solution set, taking the initial weights of the meteorological factors and the initial weights of the site factors as individuals, and fitting a function formula y through a genetic algorithm according to the initial weights;
s4.3, calculating fitness of individuals, selecting N individuals from the current population according to fitness proportion, and taking the N individuals as N parent individuals;
s4.4, carrying out random pairing on parent individuals, carrying out two-point crossover operation with larger crossover probability, and carrying out mutation operation on crossed individuals with smaller probability to obtain a plurality of new generation individuals and new generation groups, wherein the new generation individuals are updated meteorological factor weights or site factor weights, and the new generation groups are a collection of the new generation individuals;
s4.5, judging whether a termination condition is met, if yes, terminating to obtain the weight of the meteorological factors and the weight of the site factors, otherwise, returning to the step S4.3;
s4.6, obtaining a final function formula y according to the weight of the meteorological factors and the weight of the site factors, and obtaining the tree height growth model.
In this embodiment, the larger crossover probability is set to 99%, and the smaller probability in the mutation operation is set to 1%.
In step S4.2, the function formula y is:
y＝x 0 +a 1 ln b 1 g 1 +a 2 ln b 2 g 2 +,...,+a d ln b j g j +,...,+a s ln b s g s +a s+1 ln q 1 +a s+2 ln q 2 +，...，+a s+m ln q m
wherein y represents the predicted value of the current tree height, and x 0 Representing the height of the fitting tree, g j Represents the meteorological factors associated with the tree height, j=1, 2,..s, s represents the total number of meteorological factors associated with the tree height, b j Representing meteorological factor g j An influence function with age of tree, a d Representing the calculated coefficients obtained by the initial solution set, d=1, 2,..s,..s+m, { q 1 ,q 2 ,...,q m And m represents the total number of the floor factors related to the tree height.
The termination conditions in step S4.5 are: the absolute value of the difference between the predicted value and the true value of the tree height is less than 0.1m.
Step S5 comprises the steps of:
s5.1, collecting the age of a tree to be predicted, and obtaining meteorological factors and site factors of years corresponding to the age;
s5.2, inputting the meteorological factors and the site factors into a tree height growth model to obtain a tree height prediction result.
The tree height growth model is constructed based on the rational Charles model, and has better adaptability and better prediction effect for different weather and site factors. The invention can accurately predict the height of trees in different growth environments, so that maintainers have better references for making a felling maintenance plan.
Claims (5)
1. The tree height prediction method based on the rational Charles model is characterized by comprising the following steps of:
s1, collecting average tree height-age data of a tree range of a height to be predicted, and fitting parameters in a rational model through maximum likelihood estimation according to the average tree height-age data to obtain a general rational model;
s2, obtaining the tree heights of all tree age stages through a general rational model, and respectively carrying out typical correlation analysis on the tree heights and the site factors and the meteorological factors to obtain the site factors and the meteorological factors related to the tree heights;
s3, according to meteorological factors related to the tree height, acquiring a functional relation of the meteorological factors affecting the tree height along with the age of the tree, and obtaining an affecting function; s4, fitting and adding an influence function into the general rational Charles model according to a genetic algorithm, and determining the weights of meteorological factors and site factors related to the tree height to obtain a tree height growth model;
the influence function in the step S3The method comprises the following steps:
wherein k and c represent coefficients to be solved for the influence function,representing Meteorological factors->As a function of the influence of the age of the tree, j=1, 2,..s, s, the total number of meteorological factors associated with the height of the tree, t, the time; the step S4 includes the following sub-steps:
s4.1, setting a parameter space, and randomly generating N solutions to obtain an initial solution set;
s4.2, acquiring initial weights of the meteorological factors and initial weights of the site factors through an initial solution set, taking the initial weights of the meteorological factors and the initial weights of the site factors as individuals, and fitting a function formula y through a genetic algorithm according to the initial weights;
the fitting function formula through the genetic algorithm is as follows:
wherein y represents the predicted value of the current tree height,representing the height of the fitting tree,/->Represents the meteorological factors related to the tree height, j=1, 2,..s, s represents the total number of meteorological factors related to the tree height, +.>Representing Meteorological factors->Influence function according to age of tree>Representing the calculated coefficients obtained by the initial solution set, d=1, 2,..s,..s+m, { }>,/>,...,/>-represents the floor factor associated with the tree height, m represents the total number of floor factors associated with the tree height;
s4.3, calculating fitness of individuals, selecting N individuals from the current population according to fitness proportion, and taking the N individuals as N parent individuals;
s4.4, carrying out random pairing on parent individuals, carrying out two-point crossover operation with larger crossover probability, and carrying out mutation operation on crossed individuals with smaller probability to obtain a plurality of new generation individuals and new generation groups, wherein the new generation individuals are updated meteorological factor weights or site factor weights, and the new generation groups are a collection of the new generation individuals;
s4.5, judging whether a termination condition is met, if yes, terminating to obtain the weight of the meteorological factors and the weight of the site factors, otherwise, returning to the step S4.3;
s4.6, obtaining a final function formula y according to the updated weight of the meteorological factors and the weight of the site factors to obtain a tree height growth model;
s5, according to the tree height growth model, carrying out high prediction on the tree to be predicted, and obtaining a prediction result.
2. The tree height prediction method based on the rational chard model as recited in claim 1, wherein said step S2 comprises the sub-steps of:
s2.1, obtaining the tree heights of all tree age stages through a universal rational model;
s2.2, performing typical correlation analysis on meteorological factors and site factors corresponding to the tree heights at each tree age stage through an drift diameter coefficient analysis method to obtain the site factors and meteorological factors related to the tree heights.
3. The tree height prediction method based on the rational model according to claim 2, wherein the specific method for performing the typical correlation analysis on the meteorological factors and the site factors corresponding to the tree heights of the tree-age stages by the path coefficient analysis method in the step S2.2 is as follows: and according to the drift diameter coefficient analysis method, carrying out drift diameter analysis by taking the tree height of each tree age stage as a dependent variable, obtaining the drift diameter factors corresponding to each meteorological factor and each site factor, judging whether the drift diameter factors are larger than 1, if so, correlating the corresponding meteorological factors or site factors with the tree height, and otherwise, correlating not.
4. The tree height prediction method based on the rational charles model according to claim 1, wherein the termination condition in the step S4.5 is: the absolute value of the difference between the predicted value and the true value of the tree height is less than 0.1m.
5. The tree height prediction method based on the rational charles model according to claim 1, wherein said step S5 comprises the steps of:
s5.1, collecting the age of a tree to be predicted, and obtaining meteorological factors and site factors of years corresponding to the age;
s5.2, inputting the meteorological factors and the site factors into a tree height growth model to obtain a tree height prediction result.
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