Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model

Zhongyuan Gu, Miaocong Cao (), Chunguang Wang, Na Yu and Hongyu Qing
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Zhongyuan Gu: School of Investigation and Surveying Engineering, Changchun Institute of Technology, Changchun 130021, China
Miaocong Cao: School of Investigation and Surveying Engineering, Changchun Institute of Technology, Changchun 130021, China
Chunguang Wang: School of Investigation and Surveying Engineering, Changchun Institute of Technology, Changchun 130021, China
Na Yu: School of Investigation and Surveying Engineering, Changchun Institute of Technology, Changchun 130021, China
Hongyu Qing: School of Investigation and Surveying Engineering, Changchun Institute of Technology, Changchun 130021, China

Sustainability, 2022, vol. 14, issue 16, 1-12

Abstract: The extreme gradient boosting (XGBoost) ensemble learning algorithm excels in solving complex nonlinear relational problems. In order to accurately predict the surface subsidence caused by mining, this work introduces the genetic algorithm (GA) and XGBoost integrated algorithm model for mining subsidence prediction and uses the Python language to develop the GA-XGBoost combined model. The hyperparameter vector of XGBoost is optimized by a genetic algorithm to improve the prediction accuracy and reliability of the XGBoost model. Using some domestic mining subsidence data sets to conduct a model prediction evaluation, the results show that the R2 (coefficient of determination) of the prediction results of the GA-XGBoost model is 0.941, the RMSE (root mean square error) is 0.369, and the MAE (mean absolute error) is 0.308. Then, compared with classic ensemble learning models such as XGBoost, random deep forest, and gradient boost, the GA-XGBoost model has higher prediction accuracy and performance than a single machine learning model.

Keywords: mining subsidence; XGBoost; genetic algorithm; ensemble learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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