Underground space suitability assessment method based on fuzzy hierarchical synthesis method and semi-supervised learning

Abstract

The present invention discloses a method for evaluating the suitability of underground space based on a fuzzy hierarchical comprehensive method and semi-supervised learning, comprising the following steps: collecting three-dimensional data of underground space suitability evaluation indicators; normalizing the three-dimensional data to obtain normalized data, and labeling the normalized data using an improved fuzzy comprehensive evaluation method; constructing an initial suitability evaluation model based on an XGBoost model, training the initial suitability evaluation model using the labeled normalized data to obtain a suitability evaluation model; and classifying the suitability of urban underground space using the suitability evaluation model. The present invention combines an improved fuzzy hierarchical comprehensive evaluation method with an improved XGBoost model to evaluate the suitability of underground space. By improving the XGBoost model using an improved mantis algorithm, the defect that the traditional XGBoost model parameter selection base is random and difficult to obtain the optimal combination parameters can be avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban underground space development, and in particular relates to an underground space suitability assessment method based on fuzzy hierarchical synthesis method and semi-supervised learning. Background Art

[0002] The development and utilization of urban underground space has important advantages in saving resources and reducing environmental pollution, and is therefore currently being vigorously developed. However, blind development may affect the stability of building foundations, surface cracking, rock and soil instability, and may even damage the ecological environment. Therefore, it is of great significance to conduct a suitability evaluation for the development and utilization of urban underground space before planning. In terms of the perspective and dimension of suitability evaluation, most of the existing results are only two-dimensional evaluations conducted from the perspective of engineering geological environment. Traditional two-dimensional evaluations have the disadvantages of weak visualization and low evaluation accuracy. Most existing evaluation methods are supervised learning evaluation methods. The most prominent shortcoming is that traditional evaluation methods are computationally intensive and data cannot be reused, resulting in low efficiency. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes an underground space suitability assessment method based on fuzzy hierarchical synthesis method and semi-supervised learning to solve the problems existing in the above-mentioned existing technologies.

[0004] To achieve the above objectives, the present invention provides an underground space suitability assessment method based on fuzzy hierarchical synthesis method and semi-supervised learning, comprising the following steps:

[0005] Collect three-dimensional data for underground space suitability evaluation indicators;

[0006] Normalizing the three-dimensional data to obtain normalized data, and labeling the normalized data using an improved fuzzy hierarchical comprehensive evaluation method;

[0007] Building an initial suitability assessment model based on the XGBoost model, and training the initial suitability assessment model using labeled normalized data to obtain a suitability assessment model;

[0008] The suitability classification of urban underground space is carried out through the suitability assessment model.

[0009] Preferably, the three-dimensional data includes height difference, slope, rock mass characteristics, soil mass characteristics, surface water impact, groundwater depth and groundwater corrosivity.

[0010] Preferably, the method for improving the fuzzy comprehensive evaluation method includes:

[0011] Based on the principle of praise and criticism, a variable combination mathematical model is established to improve the traditional hierarchical analysis method;

[0012] The effectiveness of the traditional fuzzy comprehensive method in evaluation and grading is analyzed. When the effectiveness fails, credibility is established based on the effectiveness principle, and evaluation and grading are performed based on the credibility.

[0013] Preferably, the method for constructing the initial suitability assessment model includes optimizing the XGBoost model by improving the Mantis algorithm to obtain the initial suitability assessment model.

[0014] Preferably, the method for optimizing the XGBoost model by improving the Mantis algorithm includes:

[0015] Design the parameter range of the XGBoost model and initialize it to obtain the initialized population;

[0016] By training the initialized population using the XGBoost model, the fitness value of each individual can be obtained;

[0017] The improved mantis algorithm is used to optimize the fitness value and obtain the optimal fitness value;

[0018] A model weight is obtained based on the optimal fitness value, and the XGBoost model is updated using the model weight to obtain an initial suitability evaluation model.

[0019] Preferably, the method for improving the Mantis algorithm includes:

[0020] Use Tent mapping to initialize the Mantis algorithm population;

[0021] The solutions of finding and attacking prey in the mantis algorithm are disturbed through reverse learning;

[0022] The solution of the neutral phagocytic phase of the Mantis algorithm is perturbed by Gauss-Cauchy mutation.

[0023] Preferably, the expression of the reverse solution in reverse learning is:

[0024]

[0025] Where C ubj and C lbj are the upper and lower bounds of the current t-th iteration mantis individual in the j-th dimension, is the solution of the i-th mantis in the j-th dimension.

[0026] Preferably, the expression of the Gauss-Cauchy variation is:

[0027] X newi (t) = X b (t)×(1+μ1×Cauchy(0,δ)+μ2×Causs(0,δ))

[0028]

[0029] Where, X b (t) is the position of individual X at the tth iteration, X newi (t) is the optimal position of the t-th iteration (the position after the Gaussian-Cauchy mixture perturbation), Causs(δ) is the Gaussian mutation operator, Cauchy(δ) is the Cauchy mutation operator, δ is the standard deviation of the Cauchy-Gaussian mutation strategy, μ i is the weight coefficient, k is the adjustment coefficient greater than 0. Through experimental comparison and analysis, k = 3.2, T max is the maximum number of iterations.

[0030] Compared with the prior art, the present invention has the following advantages and technical effects:

[0031] This paper proposes a three-dimensional evaluation method for urban underground space suitability based on an improved XGBoost algorithm from a semi-supervised learning perspective. By utilizing the latest Mantis algorithm and improving it, the modified Mantis algorithm optimizes XGBoost, thereby proposing an improved XGBoost model. This method addresses the key issues of high labeling cost and low efficiency in supervised learning. It also provides new insights for urban underground space suitability evaluation and has significant practical research value. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0033] Figure 1 This is a flow chart of an underground space suitability assessment method according to an embodiment of the present invention;

[0034] Figure 2 This is a flow chart of the improved fuzzy hierarchical synthesis method evaluation system according to an embodiment of the present invention;

[0035] Figure 3 This is a flowchart of the improved XGBoost structure according to an embodiment of the present invention;

[0036] Figure 4 This is a diagram showing the effects of different parameters on the model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] Example 1

[0040] like Figure 1 As shown, this embodiment provides an underground space suitability assessment method based on fuzzy hierarchical synthesis method and semi-supervised learning, including the following steps:

[0041] Collect three-dimensional data for underground space suitability evaluation indicators;

[0042] Normalize the three-dimensional data to obtain normalized data, and label the normalized data using the improved fuzzy comprehensive evaluation method;

[0043] An initial suitability assessment model is constructed based on the XGBoost model, and the initial suitability assessment model is trained using labeled normalized data to obtain a suitability assessment model.

[0044] The suitability classification of urban underground space is carried out through the suitability assessment model.

[0045] Furthermore, the three-dimensional data includes elevation difference, slope, rock mass characteristics, soil mass characteristics, surface water impact, groundwater depth and groundwater corrosivity.

[0046] Furthermore, the method of improving the fuzzy comprehensive evaluation method includes:

[0047] Based on the principle of praise and criticism, a variable combination mathematical model is established to improve the traditional hierarchical analysis method;

[0048] The effectiveness of the traditional fuzzy comprehensive method in evaluation and grading is analyzed. When the effectiveness fails, credibility is established based on the effectiveness principle, and evaluation and grading are performed based on the credibility.

[0049] Furthermore, the present invention adopts the idea of ​​praise and criticism to construct variable weights. Assume that the weights obtained by the subjective method are g1, g2, ..., g n , the weights obtained by the objective method are s1, s2, ..., s n , the normalized combination weights are ω1, ω2, …, ω n At the same time, let the variable weight coefficients be f(x1), f(x2), ..., f(x n ), when the weight is too high and the indicator is not so important, it needs to be reduced, which is called a devaluation function. Then we have:

[0050]

[0051] On the contrary, when the weight of some indicators is relatively low but the impact of the indicators is very important, they need to be increased, which is called a reward (increasing function). Then we have:

[0052]

[0053] In addition, there may be some indicators whose weights change slightly, in which case they can be set as:

[0054] ω k ′=ω k ±f(x k ) (3≤k≤n-3) (3)

[0055] Among them, f(x i ) is a function about the indicator state value. Let the function be a parabola. When the weight decreases, it is a decreasing function about x. When the weight increases, it is an increasing function about x. When the weight does not change or changes very little, it is infinitely close to 0.

[0056] The process of improving the evaluation grading includes: introducing validity for evaluation grading; when the validity is less than 0.5, adopting the maximum membership principle for evaluation grading; when the validity is less than 0.5, constructing a credibility mathematical model based on the validity; and conducting evaluation analysis based on the credibility mathematical model.

[0057] The improved fuzzy hierarchical comprehensive method evaluation system is as follows: Figure 2 shown.

[0058] Furthermore, the method for constructing an initial suitability assessment model includes optimizing the XGBoost model by improving the Mantis algorithm to obtain the initial suitability assessment model.

[0059] The specific method is as follows:

[0060] The Mantis algorithm simulates the hunting and sexual cannibalism of mantises. It consists of four phases: the first phase is population initialization, in which the population is randomly distributed within the search space; the second phase is exploration (prey search), which mimics the mantis's search for prey; the third phase is the exploitation phase, which simulates the mantis's attack and capture behavior; and the fourth phase is sexual cannibalism. While the Mantis algorithm offers advantages such as high accuracy and fast convergence, it lacks global exploration capabilities and is prone to getting stuck in local optima. Therefore, an improved Mantis algorithm has been proposed, focusing on three phases.

[0061] The initial population of the Mantis algorithm is random, and the search for solutions by this randomly distributed population is also random, which will affect the search space. Therefore, a chaotic map is used to initialize the population. This embodiment uses a tent map to initialize the Mantis algorithm population, and its formula is as follows:

[0062]

[0063] (1) Population initialization stage

[0064]

[0065] and represents the upper and lower bounds of the j dimension; is a vector of random numbers between 0 and 1 according to a uniform distribution.

[0066] (2) Searching for prey (exploration phase): The exploration phase includes two behaviors: hiding, ambush, and pursuit.

[0067] (a) Pursuit and predation

[0068]

[0069] Formula (6) combines the Levy flight t1 and the normal distribution random number t2 to update the mantis, represents the position of the i-th mantis (solution) at the function t; · represents the product between two numbers; * represents the product of two vectors, represents a numerical vector generated based on the Levy flight strategy, |τ2| is a random number with a standard deviation of 1 and a mean of 0, and r1 and r2 are numbers randomly generated between 0 and 1 according to a uniform distribution. is a vector of randomly generated numbers in the interval (0,1) according to a uniform distribution. is a randomly selected solution from the current population such that is a binary vector generated based on the following formula:

[0070]

[0071] and is a vector randomly generated from a uniform distribution in the interval 0-1.

[0072] (b) Hidden ambush hunting

[0073]

[0074] α is a factor that controls the position of the mantis's head to cover the ambush distance. The mathematical formula of this coefficient is as follows:

[0075] α=cos(πr6)·μ (9)

[0076] r6 is a random number between 0 and 1, and μ is the distance factor. The formula is as follows:

[0077]

[0078] T is the maximum number of function evaluations, simulating the behavior of a prey moving in an environment in search of prey. The behavior of this prey to ambush distance is determined by the following formula:

[0079]

[0080] and Respectively represent the upper and lower bounds of the dimension in the problem being processed, r7 is a randomly generated number between 0 and 1, A random number vector between 0 and 1 is generated according to a uniform distribution. Represents a randomly selected solution from the archive, which is used to represent the position of the i-th mantis. Formula (9) At the beginning of the optimization iteration, the distance between the camouflaged location and the prey is large. As the number of current iterations increases, this distance will gradually decrease because it will move toward the mantis. The ambush behavior of the mantis and its prey is mathematically formulated as follows:

[0081]

[0082] r9 and r 10 is a randomly selected value uniformly distributed between 0 and 1 to achieve a trade-off between the mantis's ambush behavior and prey behavior; the behaviors of pursuers and spearers are integrated into the proposed optimizer by using a loop control factor. The loop control factor divides the optimization process into multiple parts, each of which helps to explore the possible search space of the optimization problem. Its formula is as follows:

[0083]

[0084] % represents the remainder operator, also known as the modulo operator, and P is an integer (estimated in the experimental section) representing the number of loops used to achieve the trade-off between equations (5) and (11).

[0085] (3) Attacking prey: Prey search phase

[0086] Mantis captures prey in two stages: approach and capture. The relevant parameters are as follows

[0087] d s Estimated strike distance, v s Calculate the strike speed,

[0088]

[0089] v s represents the attack speed of the mantis; ρ represents the acceleration of the mantis's impact, a constant value determined in subsequent experiments; l is a number generated between -1 and -2 to control the acceleration of gravity. s When it is close to 0, the mantis finds that it is not the right time to attack the prey. s When it is close to 1, the mantis will move quickly, attack the prey to be captured and eat it before the prey escapes; each time the mantis attacks the prey, it will be updated by the following formula:

[0090]

[0091] represents the new position of the mantis in the j dimension at time t+1, v s Indicates the attack speed of the mantis; It is the position of the prey to minimize the distance between the prey and the mantis; The attack distance varies according to the size of the mantis. The larger the mantis, the greater the attack distance. Calculated according to the following formula:

[0092]

[0093] represents the j-th dimension of the prey or the best solution obtained so far; represents the current position of the i-th mantis in the j-th dimension; when When far away from the prey, the attack distance is large, and vice versa.

[0094] When the mantis fails to attack, it needs to change direction and attack again. The formula is as follows: The mantis changes direction to attack based on the improved direction of randomly selecting two mantises from the previous group.

[0095]

[0096] and are two mantises randomly selected from the current population; r 12 It is based on a random number uniformly distributed between 0 and 1. A mantis's failure to attack means it has fallen into a local optimal solution. Individuals trapped in a local optimal solution need significant exploration and utilization capabilities to escape the optimal solution. The following mathematical model is proposed to update the mantis to take a better position to attack the prey again and prevent it from falling into the local optimal solution:

[0097]

[0098] r 13It is a randomly generated value between 0 and 1 according to a uniform distribution; Formula 9-18 is used together with the failure rate to avoid falling into local minima and to improve the convergence speed of the optimal solution of the optimization problem; the probability of Formula 9-19 gradually decreases as the current function evaluation increases to reduce the maximization search process at the beginning of the optimization process, and gradually increases the utilization operator to accelerate the convergence to the optimal solution.

[0099]

[0100] a is a predefined fixed value between 0 and 1, which is used to control the exploration and mining operations. A larger a increases exploration and minimizes mining, and vice versa. Formula (15) and Formula (17) are randomly exchanged within the MSA. r2 is a random number in the interval 0-1, and for all dimensions in each solution, r2 is a constant. r4 is a randomly generated number for each dimension in the updated solution.

[0101] 2.0 uses reverse learning to improve:

[0102] In the second and third stages of the Mantis algorithm, it is easy to fall into the local optimal solution, which reduces the accuracy of the solution. Therefore, reverse learning is used to perturb the solution to make it jump out of the local optimal solution. Suppose the reverse solution of the Mantis algorithm solution generated by direction learning is

[0103] is a feasible solution to the problem of generation t, is the solution of the i-th mantis in the j-th dimension, yes The corresponding inverse solution is The expression is:

[0104]

[0105] Among them C ubj and C lbj are the upper and lower bounds of the mantis individual in the jth dimension at the current tth iteration, i∈[1,N], N is the number of mantis individuals, j∈[1,D], D is the spatial dimension of the feasible solution.

[0106] (4) Sexual cannibalism: Female mantises eat males during or after mating. This behavior is more like sexual cannibalism. First, the female attracts her mate and attracts the male to their location. The formula is:

[0107]

[0108] Represents a female praying mantis; One randomly selected solution represents a male being attracted to a female for mating and then being eaten; is a vector representing an attraction factor, based on random numbers uniformly distributed between [0, 1]. Mate attraction is performed at the beginning of the optimization process, with the goal of attracting males to the female's location. Therefore, it is very likely to occur at the beginning of the optimization process, and the probability gradually decreases as the population increases. The mathematical formula for this probability, Pt, is the probability function:

[0109] P t =r 17 ·μ (22)

[0110] r 17 According to the random numbers uniformly distributed between [0,1], the male mates with the female through the uniform crossover operator in the genetic operator to produce new offspring. The formula is as follows:

[0111]

[0112] is a vector of random numbers uniformly distributed between [0,1]; The value of the first dimension of the first mantis represents the male mantis mating with the female. This formula is established to cover the dimensions of the convergence value within different dimensions.

[0113]

[0114] represents males; μ is the portion of males eaten; cos(2lπ) is used to give females the flexibility to turn males during feeding; male behavior focuses on reducing the risk of females provoking aggression, while several males reduce the risk of being eaten by their own kind.

[0115] 3.0 uses Gaussian Cauchy improvement:

[0116] At the end of the Mantis algorithm, it often falls into a local search state. If the previous calculation deviates from the current value, the optimization in the local area will be invalid. Therefore, in order to ensure that the local area can be jumped out again in the final stage, the search group is randomly expanded to an area far away from the target to check whether the optimal target solution is reached and achieve the effect of global development. It is necessary to perturb the last stage to jump out of the local area. Common perturbation methods include Cauchy mutation, differential mutation, Gaussian mutation, etc. Because the search range of Cauchy mutation is larger than that of Gaussian, it has better global search ability, but too large a step size can easily jump out of the optimal solution and produce a poor solution. Compared with Cauchy mutation, Gaussian mutation has better search ability in a small range. Therefore, the two are organically combined to propose a perturbation method called Gauss-Cauchy mutation. Therefore, the perturbation formula of Gauss-Cauchy is as follows:

[0117] X newi (t) = Xb (t)×(1+μ1×Cauchy(0,δ)+μ2×Causs(0,δ)) (25)

[0118]

[0119] The Cauchy density function formula is:

[0120]

[0121] Where, X b (t) is the position of individual X at the tth iteration, X newi (t) is the optimal position of the t-th iteration (the position after the Gaussian-Cauchy mixture perturbation), Causs(δ) is the Gaussian mutation operator, Cauchy(δ) is the Cauchy mutation operator, δ is the standard deviation of the Cauchy-Gaussian mutation strategy, μ i is the weight coefficient, k is the adjustment coefficient greater than 0. Through experimental comparison and analysis, k = 3.2. From the formula, it can be concluded that the early stage is mainly based on Cauchy mutation search, and the later stage is mainly based on Gaussian mutation.

[0122] Eight internationally accepted benchmark functions were selected for comparative analysis using the particle swarm optimization (PSO), northern goshawk algorithm (NGO), gray wolf optimization algorithm (GWO), praying mantis algorithm (MSA), genetic algorithm (GA), and modified mantis algorithm (CMSA). The population size of each of the six algorithms was set to 30, and the maximum number of iterations was 100.

[0123] From the analysis in Table 1, it can be seen that the mean and optimal values ​​of the improved Mantis algorithm on the benchmark test functions F1 to F8 are basically better than those of the PSO, NGO, WGO, GA, and MSA algorithms. Only the mean on the test function F1 is higher than that of PSO, but the optimal values ​​are better than those of the other five algorithms. The results further prove the superiority of the CMSA algorithm. The above results show that the use of the multi-effect improved Mantis algorithm is reliable and correct. The verification of the algorithm of the present invention verifies the improvement of the accuracy of urban underground space evaluation in subsequent semi-supervised learning.

[0124] Table 1

[0125]

[0126] Analysis of semi-supervised learning evaluation methods:

[0127] Semi-supervised learning (Semi-supervised learning) is a key research topic in pattern recognition and machine learning. It is a learning method that combines supervised and unsupervised learning. Its most prominent advantage is that it can predict a large number of unlabeled samples using a small number of labeled samples. Currently, the main classification methods for semi-supervised learning include random forests (RF), XGboost, and support vector machines. Based on bootstrap theory, Breiman combined decision trees to create the RF model. RF uses the bootstrap resampling method to extract multiple samples from the original sample. A decision tree is then constructed using a bootstrap decision tree model. The constructed decision trees are then combined and a voting method is used to obtain the final classification or prediction value. XGBoost (Extreme Gradient Boosting) is an efficient gradient boosting framework based on an improved Gradient Boosting Decision Tree (GBDT) algorithm. XGBoost, support vector machines, and clustering methods were compared in terms of effectiveness and efficiency, and XGBoost was selected as the semi-supervised evaluation method.

[0128] Improve XGboost evaluation model construction:

[0129] XGBoost is a machine learning algorithm based on the gradient boosting framework. It has the advantages of high speed, ability to process large amounts of data, and high accuracy. Since XGBoost has many parameters and different parameters have different effects on the performance of the model, its main parameters are as follows:

[0130] The learning rate (learning_rate) controls the adjustment of weights at each iteration. Different weights have an important impact on the model. A smaller learning rate can improve the stability of the model, but it will also make the model converge more slowly. On the contrary, a larger learning rate may speed up the convergence of the model, but it may also cause overfitting.

[0131] The number of trees (n_estimators) is also called the number of iterations. The number of different trees has an important impact on the model. Increasing the number of this value can improve the performance of the model but also reduce the efficiency.

[0132] Maximum tree depth (max_depth): The maximum depth of the decision tree, which controls the complexity of the tree and avoids overfitting. Generally speaking, a larger value means a more complex model and a better fit to the training data, but too high a value may also lead to overfitting.

[0133] Subsample ratio: The ratio of randomly selected samples used to train each tree. If the subsample ratio is set too small, it can prevent overfitting, but it may also reduce the performance of the model.

[0134] The feature subsampling ratio of each tree (colsample_bytree) is used to randomly extract the feature ratio when training each tree. A smaller feature subsampling ratio can increase the performance of the model.

[0135] Minimum loss reduction required for node splitting (gamma): Controls the condition for node splitting. Node splitting occurs only when the loss reduction brought by the split is greater than gamma.

[0136] from Figure 4 (a) The analysis shows that different tree depths have a great influence on the model. When the tree depth is 2, the accuracy is the lowest at 0.6579, and when it is 12, it reaches the maximum at 0.7991. When it is 12, the accuracy does not decrease but increases very little. Therefore, the range of this value is between 2 and 12. Figure 4 (b) It is found that different learning rate values ​​also have a great influence on the model. When the learning rate is 0.01, the accuracy is the lowest at 0.6382. When the learning rate increases to 0.5, it reaches the maximum at 0.9705. When the learning rate is 0.4 and 0.5, the increase is very small. Therefore, 0.01-0.5 is used as the range of this value. Figure 4 (c) It is concluded that the number of iterations also has an impact on the model. When the number of iterations is 20, the accuracy is the lowest at 0.8327. As the number of iterations increases, the accuracy stops increasing at 120, so its range is between 20 and 120. Figure 4 (d) The minimum value is 0.7196 when the sample ratio is 0.15, and the maximum value is 0.7233 when the sample ratio is 0.45 and 0.9, so the range of the value is 0.15-0.9. Figure 4 (e) It is concluded that different feature proportions have a certain impact on the model. When the subsample proportion is 0.1, the lowest accuracy is 0.7192. When the value is 0.7, the maximum value is 0.9617. When the value is 1, the accuracy is lower than the maximum value. Therefore, the range of this value is 0.1-1. Figure 4 (f) It is found that the accuracy remains unchanged when different loss thresholds are taken, indicating that the loss threshold has little impact on the model, so this parameter is not used as an indicator for model optimization.

[0137] In semi-supervised learning, using different label ratios has a certain impact on the results. Therefore, we set the label ratio to 0.2, 0.3, 0.4, 0.5, and 0.7, respectively. Accuracy increases with increasing label ratios, but the increase slows down when it reaches 0.4. Therefore, considering time efficiency, labeling cost, and accuracy, we set the label ratio to 0.4 in this experiment.

[0138] From the above content, we can conclude that setting different parameters has a great impact on the performance of the XGBoost model, and it is difficult to find the optimal combination parameters based on human experience. Therefore, the Mantis algorithm and the improved Mantis algorithm proposed in the previous article are used to optimize these parameters to obtain the optimal combination parameters. The main technical flow chart is shown in Figure 3 shown.

[0139] The model of this embodiment is verified as follows:

[0140] First, the sample data was standardized. Then, according to the analysis above, the data was designed with a labeled to unlabeled ratio of 0.4. Next, the labeled data was divided into a training set and a test set with a ratio of 0.2. The training set was then divided into a training set and a validation set with a ratio of 0.2. Finally, the developed underground space evaluation system was used for evaluation.

[0141] The Grey Wolf Algorithm, the Mantis Algorithm, and the Improved Mantis Algorithm were used to optimize XGBoost hyperparameters. Improved XGBoost models were proposed, designated Improved XGBoost-1 (WGO-XGBoost), Improved XGBoost-2 (MSA-XGBoost), and Improved XGBoost-3 (CMSA-XGBoost), respectively. To verify the reliability of the proposed models, a traditional XGBoost model and a random forest (RF) model were also used for comparative analysis. These models were used to classify the suitability of underground space development and utilization in the Airport New Town of the Chengdu Eastern New District. The results are shown in Tables 2, 3, 4, 5, and 6, respectively.

[0142] Table 2

[0143]

[0144] The results of the suitability evaluation of the development and utilization of urban underground space in Chengdu East New District Airport New City using the improved XGBoost-1 are shown in the following table:

[0145] Table 3

[0146]

[0147] The results of the suitability evaluation of the development and utilization of urban underground space in Chengdu East New District Airport New City using the improved XGBoost-2 are shown in the following table:

[0148] Table 4

[0149]

[0150] The results of the suitability evaluation of the development and utilization of urban underground space in Chengdu East New District Airport New City using the improved XGBoost-3 are shown in the following table:

[0151] Table 5

[0152]

[0153] The results of the suitability evaluation of the development and utilization of urban underground space in Chengdu Eastern New District Airport New City using random forest (RF) are shown in the following table:

[0154] Table 6

[0155]

[0156]

[0157] The above results show that the evaluation results of different evaluation methods vary significantly. To quantitatively analyze the differences among these methods, the results of XGBoost, Random Forest (RF), Improved XGBoost-1, Improved XGBoost-2, and Improved XGBoost-3 were statistically analyzed. The classification results of the five models were 85.40%, 82.13%, 87.52%, 91.35%, and 96.26%, respectively. The classification results of XGBoost were close to those of RF, verifying the reliability of the constructed XGBoost model. The classification accuracy of the Improved XGBoost model was higher than that of the traditional XGBoost model and also outperformed the RF model. The differences among the five evaluated models were 2.12%, 3.83%, 4.91%, 10.86%, and 14.13%, respectively. The results showed that Improved XGBoost-3 had the highest accuracy, improving by 10.86% over traditional XGBoost, 8.74% over Improved XGBoost-1, 4.91% over Improved XGBoost-2, and 14.13% over Random Forest. Although the accuracy of the improved XGBoost-2 is lower than that of the improved XGBoost-3, it is higher than that of the XGBoost improved by the Grey Wolf algorithm, the traditional XGBoost and the random forest model. From the above content, it can be seen that the model of the present invention can achieve the best effect.

[0158] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for underground space suitability assessment based on fuzzy hierarchical synthesis method and semi-supervised learning, characterized by: The following steps are involved: Collecting three-dimensional data for underground space suitability evaluation indicators; the three-dimensional data includes elevation difference, slope, rock mass characteristics, soil mass characteristics, surface water impact, groundwater depth, and groundwater corrosivity; Normalizing the three-dimensional data to obtain normalized data, and labeling the data using an improved fuzzy hierarchical comprehensive evaluation method; Building an initial suitability assessment model based on the XGBoost model, and training the initial suitability assessment model using labeled normalized data to obtain a suitability assessment model; The method of constructing the initial suitability assessment model includes optimizing the XGBoost model by improving the Mantis algorithm to obtain the initial suitability assessment model; The method for optimizing the XGBoost model by improving the Mantis algorithm includes: Design the parameter range of the XGBoost model and initialize it to obtain the initialized population; By training the initialized population using the XGBoost model, the fitness value of each individual can be obtained; The improved mantis algorithm is used to optimize the fitness value and obtain the optimal fitness value; Obtaining a model weight based on the optimal fitness value, and updating the XGBoost model using the model weight to obtain an initial suitability evaluation model; The method for improving the mantis algorithm comprises: Use Tent mapping to initialize the Mantis algorithm population; The solutions of finding and attacking prey in the mantis algorithm are disturbed through reverse learning; The solution of the neutral phagocytic phase of the Mantis algorithm is disturbed by Gauss-Cauchy mutation; The suitability assessment model is used to classify the suitability of urban underground space development and utilization.

2. The underground space suitability assessment method based on fuzzy hierarchical synthesis method and semi-supervised learning according to claim 1 is characterized in that: Methods for improving the fuzzy comprehensive evaluation method include: Based on the principle of praise and criticism, a variable combination mathematical model is established to improve the traditional hierarchical analysis method; The effectiveness of the traditional fuzzy comprehensive method in evaluation and grading is analyzed. When the effectiveness fails, credibility is established based on the effectiveness principle, and evaluation and grading are performed based on the credibility.

3. The underground space suitability assessment method based on fuzzy hierarchical synthesis method and semi-supervised learning according to claim 1 is characterized in that: The expression of the reverse solution in reverse learning is: Where C ubj and C lbj are the upper and lower bounds of the current t-th iteration mantis individual in the j-th dimension, is the solution of the i-th mantis in the j-th dimension.

4. The underground space suitability assessment method based on fuzzy hierarchical synthesis method and semi-supervised learning according to claim 1 is characterized in that: The expression of the Gauss-Cauchy variation is: X newi (t)=X b (t)×(1+μ1×Cauchy(0,δ)+μ2×Causs(0,δ)) Where, X b (t) is the position of individual X at the tth iteration, X newi (t) is the optimal position of the t-th iteration, Causs(δ) is the Gaussian mutation operator, Cauchy(δ) is the Cauchy mutation operator, δ is the standard deviation of the Cauchy-Gaussian mutation strategy, μ i is the weight coefficient, k is the adjustment coefficient, T max is the maximum number of iterations.

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