Title: R-WELM model for predicting water inrush from coal seam floor

Authors: Jiancong Fan; Qiang Wang; Yang Li

Addresses: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong Province, 266590, China; Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao, China ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong Province, 266590, China ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong Province, 266590, China; Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao, China

Abstract: Water level variation in explorational boreholes serves as a direct indicator of water inrush risk in mining sites. Despite recent efforts to predict mine water inrush accidents using machine learning models based on diverse observational data, including borehole water levels, the accuracy, and timeliness of forecasting major accidents still need improvement. By integrating rough set theory to enhance the weighted extreme learning machine (WELM) model, a novel prediction model for coal seam floor water inrush is proposed. Firstly, the rough set theory calculates the roughness of positive and negative samples of bottom water inrush data. Then, the importance of the bottom water inundation data samples is calculated based on the roughness and approximation accuracy. Subsequently, the weight matrix is constructed using the sample importance and the number of samples, which is used to refine the WELM to improve classification prediction accuracy further. The experimental results show that the accuracy of the rough set weighted extreme learning machine (R-WELM) surpasses the traditional model, achieving a result of 91.66%. The method holds significant potential for diverse applications and can be a crucial reference for predicting water inrush in coal bed floors.

Keywords: extreme learning machine; ELM; water bursting from coal seam floor; water inrush prediction; rough set; weighted extreme learning machine; WELM.

DOI: 10.1504/IJBIC.2024.140131

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.1, pp.12 - 21

Received: 23 Jan 2024
Accepted: 08 Apr 2024
Published online: 24 Jul 2024 *

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