## 1 Introduction
In the field of coal mine safety, there has been considerable research on deformation monitoring using fiber-optic sensing. [PERSON] [16] proposed a fiber-optic-based PVC pipe strain monitoring system, installing the proposed sensor in mine tunnels to monitor rock mass movement. [PERSON] [17] used fiber-optic sensors on mine support elements to monitor pressure changes on the support, equipped with visual displays for monitoring results, and conducted emergency predictions based on these results. [PERSON] et al. [18] proposed a fiber-optic sensor model for monitoring the stress of mine working machinery, ensuring a new generation of computer-aided measurement for underground mining. [PERSON] [19] introduced fiber-optic sensing technology to monitor the deformation of roadway surrounding rock, timely grasping the internal deformation characteristics of deep roadway surrounding rock and providing a scientific basis for stability control decisions of deep roadway surrounding rock. [PERSON] et al. [20] proposed a fiber-optic sensing-based method for partitioning voids in mining overlying rock mass, and combined with physical model tests, systematically investigated the size, distribution, and deformation characteristics of rock-mass voids, concluding that the rate of void expansion is affected by the change of stress and that stress regulation is a key factor in preventing void expansion and rock mass destabilization. [PERSON] et al. [21] studied the stress distribution law and motion characteristics of the strata under reamer pillar mining by physical similarity simulation experiments using distributed fiber-optic sensing technology and concluded that the deformation of the coal pillar would go through three stages under the safety critical state. [PERSON] et al. [22] used the Brillouin light time-domain reflection method for dynamic deep coal seam mine floor mining damage and concluded that the accuracy of this method is improved by 7-10% compared with the traditional monitoring method. Simultaneously, fiber-optic grating monitoring encounters certain noise redundancy issues, and accurate deformation data prediction is crucial for safe underground mining [23; 24]. Therefore, it is essential to select suitable and efficient noise reduction algorithms to filter and reduce noise from the monitored signals, thereby improving the accuracy of data prediction. Common noise reduction methods include f-x domain predictive filtering [25], wavelet transform [26], data denoising methods based on empirical mode decomposition (EMD) [27], and ensemble empirical mode decomposition (EEMD) [28; 29; 30; 31; 32]. These studies have explored denoising decomposition algorithms in terms of the modal aliasing, decomposition completeness, and signal distortion of the IMF components after algorithm decomposition. However, there is a lack of appropriate threshold selection for the decomposed IMF components. In summary, based on the deformation of section coal pillars in the Huojitu District of Daliuta Coal Mine, as the engineering background, this study uses fiber-optic grating sensing technology for real-time monitoring. It introduces the distribution entropy (DE) [33] combined with EEMD to construct a denoising algorithm model. The DE algorithm is used to filter the effective IMF components from the EEMD decomposed IMF components, eliminating the components with a high noise ratio, and reconstructing a signal with high fidelity. Subsequently, singular value decomposition (SVD) is employed for secondary noise reduction, laying the groundwork for subsequent optical fiber grating sensing prediction studies of section coal pillar deformation. This study aims to optimize the long short-term memory neural network with the dung beetle optimization (DBO) algorithm to construct a deformation prediction model for section coal pillars.