CN102708277A - Inversion design method for snow depth based on ant colony algorithm - Google Patents

Abstract

The invention discloses an inversion design method for snow depth based on an ant colony algorithm, relating to the fields of intelligent computing and passive microwave remote sensing of snow. The method comprises the steps of obtaining the analog data of the relationship between the brightness temperature in different frequencies and the snow depth through a microwave radiation transmission model of the snow, discretizing the analog brightness temperature data and the snow depth data obtained through simulation, converting the analog brightness temperature data and the snow depth data into a routing space of ants in the ant colony algorithm, regarding each discrete interval as a possible path point, setting initial values of the pheromone concentration at each possible path point, wherein the path selected by each ant represents an inversion rule, calculating the validity of the rule as the foundation for updating the pheromone concentration, feeding back the path selection of the next ant colony and iterating for many times,to obtain the final inversion rule of the snow depth.

Description

Snow depth inverting method for designing based on ant group algorithm

Technical field

The present invention designs the passive microwave remote sensing two big fields of intelligence computation and accumulated snow.What relate generally to is the method for designing of snow depth inverting.

Background technology

As one of important parameter of accumulated snow, snow depth information is significant for the assessment of global change research due and snow-broth resource.How exactly the inverting snow depth is the important content in the accumulated snow Remote Sensing Study always.

In the accumulated snow Remote Sensing Study, passive microwave remote sensing is a very active research field, has become one of main research means of accumulated snow remote sensing.Existing algorithm has the NASA algorithm, MEMLS model, HUT model, compact medium radiation delivery model etc.

Ant group algorithm is a cluster intelligent algorithm, algorithm simulation the ant crowd in the optimizing ability that search is embodied during food source, be used for solving the difficult problem that discrete system is optimized.The applied research of relevant ant group algorithm at present shows, the ant colony intelligence algorithm has significant superiority aspect the complicated optimum problem finding the solution.As a kind of global search algorithm, ant group algorithm can effectively be avoided the excellent appearance of separating of local pole, and certain wrong computing several times can not have influence on integral result, and very high fault-tolerance is arranged.As a height nonlinear problem, the snow depth inverting can't obtain an accurate explicit inversion formula, and ant group algorithm adopts probability simulation method to carry out computing, is close to complicated truth more.Therefore, ant group algorithm has original advantage in snow depth inverting research.

Summary of the invention

The present invention is applied to ant group algorithm in the snow depth inverting method for designing of passive microwave remote sensing, and the advantage of this method is:

1. algorithm flow is clear, is easy to realize;

2. have stronger portability, incoming frequency can change according to actual conditions;

3. with respect to other snow depth inverse models based on statistics, the scope of application of algorithm is wider;

Concrete steps based on the design of the snow depth inversion algorithm of ant group algorithm comprise:

Concrete steps based on the snow depth inverting method for designing of ant group algorithm comprise:

1) simulated data that accumulated snow passive microwave radiation delivery Model Calculation is obtained is carried out discretize.The data that passive microwave radiation delivery modeling through accumulated snow obtains; Combination for each frequency radiation brightness-snow depth; When carrying out discretize, consider the requirement of algorithm operational efficiency and arithmetic accuracy two aspects, with certain burst length; Radiation brightness and snow depth are divided into the interval of series of discrete, constitute the path spacing of ant group algorithm.As the path node of ant among the ant crowd, the snow depth interval is as the terminal point in path between the discrete regions of bright temperature;

2) the pheromone concentration initialization of the parameter setting of algorithm and path node.Before ant group algorithm begins iteration, ant quantity K among the ant crowd is set, maximum iteration time N, smallest sample ratio C, pheromones volatility coefficient ρ is initialized as identical value with the pheromone concentration of all path nodes:

${\mathrm{\τ}}_{\mathrm{ij}}(t=0)=\frac{1}{{\mathrm{\Σ}}_{i=1}^a{b}_i}$

Wherein, τ _IjBe t=0 moment path node term _IjPheromone concentration, a is the interval sum of the snow depth divided, b _iIt is all possible value in i the snow depth interval;

3) according to the statistical nature of training sample, construct the heuristic function of ant group algorithm, the heuristic function that defines each path node is:

${\mathrm{\&eta;}}_{\mathrm{ij}}=\frac{\max ({\mathrm{\&Sigma;}}_n\mathrm{freq}{\mathrm{<figure-callout\; id="1"\; label="T\; ij"\; filenames="HSA00000696338900011.png"\; state="\{\{state\}\}">T</figure-callout>}}_{\mathrm{ij}}^{},{\mathrm{\&Sigma;}}_n\mathrm{freq}{T}_{\mathrm{ij}}^2,\&CenterDot;\&CenterDot;\&CenterDot;{\mathrm{\&Sigma;}}_n\mathrm{freq}{T}_{\mathrm{ij}}^w\&CenterDot;\&CenterDot;\&CenterDot;{,\mathrm{\&Sigma;}}_n\mathrm{freq}{T}_{\mathrm{ij}}^k,)}{\mathrm{\&Sigma;}{T}_{\mathrm{ij}}}$

Wherein, η _IjExpression path node term _IjThe heuristic function value, T _IjFor satisfying path node term _IjThe sample number of representative condition,

Be T _IjMiddle snow depth drops on the sample number among the target interval w.In the algorithm operational process, whenever obtain a final rule, all will legal sample be removed, therefore, the heuristic function value will dynamically update after the final rule obtaining;

4) in the process in structure path, each path node is joined in the path condition node term with certain probability _IjSelecteed probability calculates by following formula:

$P_{\mathrm{ij}}\left(t\right)=\frac{{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)\&times;{\mathrm{\&eta;}}_{\mathrm{ij}}\left(t\right)}{{\mathrm{\&Sigma;}}_{i=1}^a{\mathrm{\&Sigma;}}_{j=1}^b{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)\&times;{\mathrm{\&eta;}}_{\mathrm{ij}}\left(t\right)}$

Wherein, τ _Ij(t) be t path node term constantly _IjPheromone concentration, η _Ij(t) be the t heuristic function value of path node termij constantly.

When a complete path produces, be an inverting rule.But, make the inverting rule become too complicated, and some unessential condition entry also may have negative effect to the result because each condition node all has been chosen in the rule.Therefore need prune the inverting rule that has generated.Through calculating the validity of inverting rule, reject the condition entry that regular validity is improved then, till removing any condition entry validity is reduced.The validity of inverting rule is according to computes:

$Q=\left(\frac{\mathrm{TurePos}}{\mathrm{TruePos}+\mathrm{FalseNeg}}\right)\&times;\left(\frac{\mathrm{TrueNeg}}{\mathrm{FalsePos}+\mathrm{TrueNeg}}\right)$

Wherein, TruePos representes to meet rule condition and the correct sample number of inversion result; FalsePos representes to meet rule condition but the incorrect sample number of inversion result; FalseNeg representes not meet rule condition, but snow depth value and the consistent sample number of inverting rule expected results; TrueNeg representes not meet rule condition, and the snow depth value also with the different sample number of result of inverting rule;

6) after whole ant crowd goes out the inverting rule through an iterative construction; The pheromone concentration of all condition entries is upgraded according to the validity of rule; Selected condition entry pheromone concentration in rule is increased, and not selected condition entry pheromone concentration reduces.Pheromone concentration according to following formula update condition item node:

${\mathrm{\&tau;}}_{\mathrm{ij}}(t+1)=(1-\mathrm{\&rho;})\&CenterDot;{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)+\left(\frac{\mathrm{<figure-callout\; id="1"\; label="Q"\; filenames="HSA00000696338900011.png"\; state="\{\{state\}\}">Q</figure-callout>}}{1+Q}\right)\&CenterDot;{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)$

Wherein, ρ is the volatility coefficient of pheromones, and Q is the validity of the inverting rule at condition entry place.After the pheromone concentration of all conditions item node all is updated, the beginning next iteration;

7) if the algorithm iteration number of times less than maximum iteration time N; And when not being selected into number of training proportion c in the path, return 3 greater than preset value C) proceed iteration, otherwise algorithm flow finishes; The final path that output produces constitutes the inverting list of rules;

Description of drawings

Fig. 1 is the snow depth inversion algorithm design flow diagram based on ant group algorithm.

Embodiment

Below, in conjunction with summary of the invention, the snow depth inverting method for designing based on ant group algorithm being experimentized, Fig. 1 is the snow depth inversion algorithm design flow diagram based on ant group algorithm, in conjunction with Fig. 1 the present invention is done further description.

A. the simulated data discretize of passive microwave radiation delivery model, process is following:

1. use the MEMLS model, with reference to the parameter setting of AMSR-E sensor, simulation obtains 18.7GHz, and the relation of the level of 36.5GHz, the bright temperature of vertical polarization and snow depth is as training sample.The snow depth variation range is made as 3～330cm;

2. be step-length with 1cm, snow depth is dispersed turns to a series of intervals; With 5K is step-length, and the bright temperature value of each frequency is dispersed turns to a series of intervals.With the path spacing of the interval after the discretize as ant crowd in the ant group algorithm, as a path node, each snow depth interval is as a path termination between each bright warm area;

B. ant group algorithm is relevant is provided with:

1. ant quantity K=1000 among the ant crowd is set, maximum iteration time N=1000, smallest sample ratio C=10%, pheromones volatility coefficient ρ=0.1;

2. initialization current iteration frequency n=1, the ant quantity k=1 of current process among the initialization ant crowd, the current not selected training sample ratio c=100% of initialization;

3. according to top algorithm steps explanation, utilize ant group algorithm to obtain the inverting regular matrix;

C. snow depth inverting experiment and accuracy test:

1. the snow depth measured data of testing with reference to CLPX (The Cold Land Processes Field Experiment); 18.7GHz with AMSR-E sensor four data points in corresponding geographic position; 36.5GHz in the bright temperature data of level and the vertical polarization substitution inverting regular matrix, the inverting value SD=[67.5,157.5 of output snow depth; 133.5,172.5] and cm;

2. the inversion result of snow depth and the measured result of CLPX experiment are compared, the contrast situation is as shown in table 1.Through calculating, the average inversion accuracy of algorithm is 92.04%.

Table 1: based on the snow depth inversion algorithm inversion result of ant group algorithm

Claims (8)

1. the snow depth inverting method for designing based on ant group algorithm is characterized in that this method comprises the steps:

1) simulated data that accumulated snow passive microwave radiation delivery Model Calculation is obtained is carried out discretize; Path spacing as ant crowd in the ant group algorithm; All as a possible path point, each snow depth interval is all as a possible path termination between each bright warm area that the process discretize obtains;

2) the volatility coefficient ρ of plain concentration τ of the initial information of each path point and pheromones in the set path space; And setting initial parameter, i.e. ant quantity K, maximum cycle N and smallest sample ratio C among the ant crowd;

3) according to the statistical nature of simulated data, construct the heuristic function η of ant group algorithm, calculate the heuristic function value of each condition node;

4) according to the pheromone concentration and the heuristic function value of path node, calculate this node and be chosen to the probability P in the path, ant is selected the joint structure path according to this probability;

5) after paths structure is accomplished, calculate the validity Q in this path, and rule is pruned;

6) when whole ant crowd through after iteration, according to the validity Q of rule under the volatility coefficient ρ of pheromones and the node pheromone concentration of all nodes is upgraded;

7) when reaching maximum iteration time or be not selected into number of training in the path less than preset value, algorithm flow finishes, and the final path that this moment, ant group algorithm produced constitutes the inverting list of rules, and output is through the inverting value of the snow depth optimized.

2. based on the described a kind of snow depth inverting method for designing of claim 1, it is characterized in that based on ant group algorithm: in the said step 1), the data that the passive microwave radiation delivery modeling through accumulated snow obtains; Combination for each frequency radiation brightness-snow depth; When carrying out discretize, consider the requirement of algorithm operational efficiency and arithmetic accuracy two aspects, with certain burst length; Radiation brightness and snow depth are divided into the interval of series of discrete; Constitute the path spacing of ant group algorithm, as the path node of ant among the ant crowd, the snow depth interval is as the terminal point in path between the discrete regions of bright temperature.

3. based on the described a kind of snow depth inverting method for designing based on ant group algorithm of claim 1, it is characterized in that: said step 2), before ant group algorithm carried out iteration, the pheromone concentration of each path node was initialized as identical value:

${\mathrm{\&tau;}}_{\mathrm{ij}}(t=0)=\frac{1}{{\mathrm{\&Sigma;}}_{i=1}^a{b}_i}$

Wherein, τ _Ij(t=0) be t=0 path node term constantly _IjPheromone concentration, a is the interval sum of the snow depth divided, b _iIt is all possible value in i the snow depth interval.

4. based on the described a kind of snow depth inversion algorithm method for designing based on ant group algorithm of claim 1, it is characterized in that: in the said step 3), the heuristic function that defines each path node is:

${\mathrm{\&eta;}}_{\mathrm{ij}}=\frac{\max ({\mathrm{\&Sigma;}}_n\mathrm{freq}{T}_{\mathrm{ij}}^1,{\mathrm{\&Sigma;}}_n\mathrm{freq}{T}_{\mathrm{ij}}^2,\&CenterDot;\&CenterDot;\&CenterDot;{\mathrm{\&Sigma;}}_n\mathrm{freq}{T}_{\mathrm{ij}}^w\&CenterDot;\&CenterDot;\&CenterDot;{\mathrm{\&Sigma;}}_n\mathrm{freq}{T}_{\mathrm{ij}}^k,)}{\mathrm{\&Sigma;}{T}_{\mathrm{ij}}}$

Wherein, η _IjExpression path node term _IjThe heuristic function value, T _IjFor satisfying path node term _IjThe sample number of representative condition,

Be T _IjMiddle snow depth drops on the sample number among the target interval w.In the algorithm operational process, whenever obtain a final rule, all will legal sample be removed, therefore, the heuristic function value will dynamically update after the final rule obtaining.

5. based on the described a kind of snow depth inverting method for designing based on ant group algorithm of claim 1, it is characterized in that: in the said step 4), ant joins each path node in the path with certain probability path node term when the structure path _IjSelecteed probability is:

$P_{\mathrm{ij}}\left(t\right)=\frac{{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)\&times;{\mathrm{\&eta;}}_{\mathrm{ij}}\left(t\right)}{{\mathrm{\&Sigma;}}_{i=1}^a{\mathrm{\&Sigma;}}_{j=1}^b{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)\&times;{\mathrm{\&eta;}}_{\mathrm{ij}}\left(t\right)}$

Wherein, τ _Ij(t) be t path node term constantly _IjPheromone concentration, η _Ij(t) be t path node term constantly _IjThe heuristic function value.

6. based on the described a kind of snow depth inverting method for designing based on ant group algorithm of claim 1, it is characterized in that: in the said step 5), when a complete path produces, be an inverting rule, the validity of inverting rule is according to computes:

$Q=\left(\frac{\mathrm{TurePos}}{\mathrm{TruePos}+\mathrm{FalseNeg}}\right)\&times;\left(\frac{\mathrm{TrueNeg}}{\mathrm{FalsePos}+\mathrm{TrueNeg}}\right)$

Wherein, TruePos representes to meet rule condition and the correct sample number of inversion result; FalsePos representes to meet rule condition but the incorrect sample number of inversion result; FalseNeg representes not meet rule condition, but snow depth value and the consistent sample number of inverting rule expected results;

TrueNeg representes not meet rule condition, and the snow depth value also with the different sample number of result of inverting rule;

Utilize the validity of rule that the inverting rule that produces is pruned: to reject successively and can make validity obtain the maximum path node that improves, till removing validity that any node all can make rule and reducing.

7. based on the described a kind of snow depth inverting method for designing of claim 1 based on ant group algorithm; It is characterized in that: in the said step 6); After whole ant crowd went out the inverting rule through an iterative construction, the pheromone concentration of all path nodes upgraded according to following formula:

${\mathrm{\&tau;}}_{\mathrm{ij}}(t+1)=(1-\mathrm{\&rho;})\&CenterDot;{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)+\left(\frac{Q}{1+Q}\right)\&CenterDot;{\mathrm{\&tau;}}_{\mathrm{ij}}\left(t\right)$

Wherein, ρ is the volatility coefficient of pheromones, and Q is the validity of the inverting rule at path node place, τ _Ij(t+1) be t+1 path node term constantly _IjPheromone concentration.

8. based on the described a kind of snow depth inverting method for designing of claim 1 based on ant group algorithm; It is characterized in that: in the said step 7); Algorithm has at first been preset maximum iteration time and minimum training sample, when iteration proceeds to maximum iteration time, when perhaps not selected ratio has reached preset value in the training sample; Algorithm finishes, and the inverting rule that produce this moment is as final inverting rule.

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