An improved LSSVM discrimination model based on factor analysis and moth flame optimization algorithm for identifying water inrush sources across multiple aquifers in mines

作     者：Bi, Yaoshan Shen, Shuhao Wu, Jiwen 

作者机构：Huainan Normal Univ Sch Mech & Elect Engn Huainan 232001 Peoples R China Suzhou Univ Sch Informat Engn Suzhou 234000 Peoples R China Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China 

出 版 物：《ENVIRONMENTAL EARTH SCIENCES》 (环境地质学)

年 卷 期：2024年第83卷第14期

页      面：424-424页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0709[理学-地质学] 081803[工学-地质工程] 07[理学] 08[工学] 0708[理学-地球物理学] 0818[工学-地质资源与地质工程] 0815[工学-水利工程] 

基　　金：Doctoral Research Initiation Fund of Suzhou University [2021BSK043] Anhui Provincial Department of Education University Research Project (Natural Science) [2023AH052246] 

主　　题：MFO algorithm LSSVM model Factor analysis Water inrush Water source discrimination Coal mine 

摘      要：To accurately and swiftly identifying the source of water inrush in mines, a discrimination model based on factor analysis (FA) and the moth flame optimization (MFO) algorithm coupled with least squares support vector machine (LSSVM) is proposed. Drawing from the hydrogeological conditions of the Yuanyi Mine in the Huaibei Mining Area, water samples from three aquifers were collected, and ten hydrochemical indicators were selected for the purpose of identifying the water inrush sources. Firstly, FA was performed on these ten indicators to extract five new components that can comprehensively reflect most of the information of all indicators, eliminating redundant information between the original indicators. Then, the extracted new components was used as inputs to the LSSVM model. Finally, the MFO algorithm was used to automatically optimize the two pivotal parameters of the penalty factor C and the kernel function parameter g of LSSVM, and a discrimination model based on FA-MFO-LSSVM was established. Furthermore, a comparative analysis of the discrimination performance of the FA-MFO-LSSVM model against five other models was carried out. The results unequivocally indicate that the FA-MFO-LSSVM model has high discrimination accuracy for both training and testing samples, and compared to the other five models, this model exhibits stronger discriminative performance.