## 4 Prediction Model Establishment
### GA-LSSVM Combination Forecasting Algorithm  
In this paper, we propose a method for predicting dust concentration based on the genetic optimization least squares support vector machine. The core idea of the algorithm is to utilize the global search ability of genetic algorithms to address the issue of LSSVM parameter selection. It aims to automatically optimize the parameters to find the most suitable model configuration, enhancing the accuracy and stability of model predictions. As an enhanced variant of support vector machines, LSSVM is solved by minimizing the squared error. In the regression prediction problem, it is necessary to determine a hyperplane that best fits the training dataset. This involves two main parameters: penalty factor and kernel parameter. The choice of these parameters is crucial for the performance of the model. As a global optimization search algorithm, GA can optimize the parameters of LSSVM. In the genetic algorithm, each individual represents a set of specific parameter values (such as the value of \(\gamma\) and \(\sigma\)), and these individuals constitute an initial population. Through multiple iterations of evolution, GA continuously searches and updates the optimal parameters of the LSSVM, aiming to gradually enhance the prediction performance of the model on the training set.  
The specific steps of the model prediction are as follows:  
Step 1. Data normalization: collect the sample data for the time series and perform noise reduction processing, such as data normalization.  
Step 2. LSSVM model construction: establish an LSSVM (least squares support vector machine) prediction model.  
Step 3. Hereditary operation: utilizing the LSSVM prediction model to train the training sample data, obtain the root mean square error and correlation coefficient for both the training and test samples.  
Step 4. Genetic manipulation: Integrate a GA to optimize the parameter of LSSVM prediction models The initial values of the optimized parameters are set, and the LSSVM model is reconstructed using the optimal parameters optimized by GA. Then, train and test the model, the model's generalization ability and prediction accuracy have been verified. And output the root mean square error and correlation coefficient of the GA-optimized model.  
Step 5. Model evaluation: analyze and evaluate all error results.  
The training of models, such as support vector machines and random forests, requires a large number of datasets in the training libraries to ensure the applicability of the model. This type of model involves two datasets: the training set for algorithm learning and the test set for algorithm prediction. The research must ensure that there is an adequate amount of training data for learning and training, and data for establishing the transfer matrix. Therefore, the training sample set and the test sample set are selected at a 7:3 ratio. For example, out of the 250 sets of data newly monitored by dust in August 2023, the first 7 days of the collected 10-day data (approximately 180 sets of data) are used as the training set, and the last 3 days are used as the test set (approximately 70 sets of data). The modeling process is shown in Figure 8 [16].