Research on a dust concentration prediction model for open-pit mines based on error reciprocal integration GA-LSSVM and Elman-Adaboost - Zhiguo, Chang; Shuangshuang, Xiao; Jin, Liu (Frontiers in Environmental Science, 2024)
The prediction of dust concentration in open-pit mine is a critical foundation for minimising dust pollution. In order to improve the prediction accuracy of dust concentration in an open-pit mine, the combined prediction algorithm model of GA-LSSVM and Elman-Adaboost based on the integration of error reciprocal approach was investigated. Firstly, the monitoring equipment of dust concentration and meteorological factors was installed in the open-pit mine site to collect important data, and the distribution law of dust concentration, meteorological and production intensity data was analyzed. The mutual information feature screening algorithm was utilised to efficientlyly remove the redundant and disruptive model prediction performance. The characteristic variables, according to the importance of information, select four indicators of stripping amount, temperature, humidity and wind direction, and then determine the input variables of the prediction model. The dust concentration prediction model was then developed using the genetic algorithm optimised least squares support vector machine (GA-LSSVM) and the Elman neural network optimised adaptive enhancement algorithm (Elman-Adaboost) models. The final prediction results were integrated using the error reciprocal method, and then the combined prediction model of dust concentration in open-pit mine in winter was constructed. Finally, the sample data is divided into a training set and a testing set in a 7:3 ratio to predict dust concentration, and the model evaluation index and test method are proposed. The results show that, using PM2.5 as an example, the model’s input variables are historical PM2.5 concentration data and external environmental factors selected based on mutual information. The evaluation indexes of the model include the correlation coefficient R2, root mean square error RMSE, and standard deviation SD. The combined model had an R2 of 0.893, RMSE of 11.697, and SD of 22.174. Compared to the GA-LSSVM model and Elman-Adaboost models, the R2 increased by 24.5% and 41.2% respectively, while the RMSE decreased by 31.0% and 36.7% respectively. When compared to the original sample data set SD (23.528), it is evident that the combined model clearly has higher prediction accuracy.