## 1 Introduction  
In the process of construction and development of modern cities, ground surface settlement has become a serious urban problem, which has widely appeared in major cities around the world [1]. Due to the combination of factors such as the concentration of urban buildings, over-exploitation of groundwater resources, and weak urban foundation, the impact of ground surface settlement is increasingly significant [2]. Long-term surface subsidence directly leads to road collapse, building tilting, and building cracking. This causes serious geological disasters, which seriously threaten urban stability and safety, such as through building collapse [3]. At the same time, they may also indirectly lead to the degradation of groundwater resources and environmental pollution. Therefore, timely monitoring of urban land surfaces is crucial for identifying potential risks of geological disasters, which eliminates safety hazards in urban construction and development. Traditional methods of surface subsidence monitoring, such as geodetic survey and Global Navigation Satellite System (GNSS), have high accuracy. These are labor-intensive, high-cost, long-cycle, and low-efficiency methods, especially for long-term series monitoring. The area monitored by traditional methods is easily limited, and the amount of monitoring data generated is small [4]. With the development of global microwave remote sensing satellite technology, large-area long-time-series monitoring has become possible. The Interferometric Synthetic Aperture Radar (InSAR) technique has found extensive application in the surveillance of surface subsidence [5]. As a typical case of urban land subsidence in the western plateau faulted basin of China, Kunming City has shown a wide subsidence range and a fast subsidence rate [6]. In recent years, the surface settlement of the Kunming urban area has obviously expanded and accelerated. This makes it particularly important to study the surface settlement of the Kunming urban area, which meets the needs of the modern economic and social construction of \"one lake and four areas\" [7]. [PERSON] et al. and [PERSON] et al. have collected settlement data in Kunming City with the Sentinel-1 radar by PS-InSAR and SBAS-InSAR, analyzed the surface settlement of urban areas, and verified the effectiveness of this method in monitoring small deformations [8; 9]. However, due to the timeliness of remote sensing technology, the formation of land surface settlement always lags behind the actual situation. The application has limitations, meaning it cannot reflect the current actual situation and achieve real-time monitoring [10]. The prediction of land surface subsidence trends has become an important research subject. With the widespread use of neural network models, Long Short-Term Memory (LSTM), used as a dedicated neural network for processing time series data, has demonstrated its unique advantages in feature learning [11; 12]. However, the traditional LSTM may affect the stability of model prediction due to improper parameter selection. To overcome this challenge, Grey Wolf Optimizer (GWO) enhances the parameter selection procedure of the LSTM model. GWO is a heuristic optimization algorithm, which mimics the social hierarchy and hunts patterns observed in grey wolves within their natural habitat and convergence characteristics [13]. By applying GWO to the parameter optimization of the LSTM model, the performance and accuracy of the prediction model can be significantly improved. This paper aims to construct GWO-optimized LSTM and a new method of Time Series-Interferometric Synthetic Aperture Radar (TS-InSAR). Taking Kunming City as an example, the land surface subsidence prediction is studied, based on ESA Sentinel-1A data, Precise Orbit Determination (POD), and JAXA-published ALOS World 3D-30 m (AW3D30) data as an external digital elevation model (DEM). These data serve as the basis for building and analyzing the prediction model of land subsidence.