Research on Ground Subsidence Prediction Model in Xinmi City based on TSC-LSTM
- Pages: 1-12(2024)
Published Online： 02 April 2024
DOI： 10.11834/jrs.20243530
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摘要
地面沉降预测对于城市地面沉降模式的深入分析和早期预警具有重要的指导意义。传统的数值预测模型在捕捉地面沉降数据复杂特征方面存在一定困难，导致预测结果的准确性不高。为了解决这一问题，本研究基于SBAS-InSAR方法获取的新密市地面沉降信息，构建了结合趋势和季节特征长短时记忆网络的地面沉降预测模型（TSC-LSTM，Trend Seasonal Characteristics-LSTM），该模型联合基于加权回归季节趋势分解（STL）在沉降数据时序特征提取方面的优越性和长短期记忆模型（LSTM）在时序预测方面对梯度消失问题的处理优势，实现对地面沉降数据更准确的预测。结果表明：（1）新密市2018年到2022年的地面沉降速率为-60.3 ~ 51.96 mm/a，共形成五个地面沉降中心区域。其中，最大累计沉降和最大累积隆起分别为304.9 mm和197.68 mm。（2）TSC-LSTM模型在五个沉降中心区域的预测中表现出色，TSC-LSTM模型的R²值范围为0.9985 ~ 0.9992，明显高于次优模型LSTM的0.9662 ~ 0.9872。TSC-LSTM模型预测精度的RMSE值小于2 mm，达到了1.2426 ~ 1.7403 mm。（3）单点预测结果表明，TSC-LSTM模型能够更精确的把握累积沉降数据中的局部变化趋势。本研究提出的方法能为城市地面沉降的深入研究提供有力支持。
Abstract
Predicting land subsidence is crucial for conducting in-depth analyses and providing early warnings about urban land subsidence patterns. However
traditional numerical prediction models often struggle to accurately capture the intricate characteristics of land subsidence data
leading to less precise predictions. This study focuses on Xinmi City and endeavors to improve the accuracy of land subsidence prediction by combining time series feature extraction methods with time series prediction techniques.In this study
232 interference images provided by HyP3 were utilized to acquire land subsidence information in Xinmi City spanning from January 2018 to December 2022
employing SBAS-InSAR technology. Recognizing the challenge of achieving high accuracy in directly predicting land subsidence data
this study developed a land subsidence prediction model that integrates trend and seasonal characteristics using Long Short-Term Memory networks (TSC-LSTM
Trend Seasonal Characteristics-LSTM). The TSC-LSTM model capitalizes on the strengths of weighted regression seasonal trend decomposition (STL) for extracting time series features from settlement data and the long short-term memory model (LSTM) for addressing the vanishing gradient problem in time series prediction. This fusion of techniques allows for a more precise analysis of land subsidence data and enables highly accurate predictions. Distinguishing itself from the conventional LSTM model
the TSC-LSTM model refrains from directly inputting ground subsidence data. Instead
it employs STL to meticulously extract both trend and seasonal characteristics from the land subsidence data. This approach maximizes the utilization of characteristic information inherent in the land subsidence data. Subsequently
these features are fed into the LSTM model for prediction. This unique methodology reduces noise interference and significantly enhances the accuracy of model predictions.This research leverages time-series InSAR data from 2018 to 2022 for Xinmi City
employing the TSC-LSTM model
deep learning architectures (RNN and LSTM)
and conventional machine learning algorithms (MLP and SVR) to forecast the cumulative subsidence data for five subsidence centers using SBAS-InSAR. It identifies the two most optimal models and validates their efficacy in single-point prediction scenarios
utilizing domain-specific terminology. Research findings indicate the following:(1) Between 2018 and 2022
Xinmi City experienced a land subsidence rate ranging from -60.3 to 51.96 mm per annum
resulting in the identification of five distinct land subsidence center areas. Among these
the highest cumulative settlement and uplift reached 304.9 mm and 197.68 mm
respectively. The universality of the TSC-LSTM model across diverse datasets has been corroborated
demonstrating its high precision
exceptional generalization capability
and stable high performance in the prediction of land subsidence
employing specialized terminology. (2) The TSC-LSTM model exhibited exceptional performance in predicting the five subsidence center areas. The R² values for the TSC-LSTM model range from 0.9985 to 0.9992
significantly surpassing the second-best model
LSTM
which has an R² range of 0.9662 to 0.9872. Moreover
the RMSE values for the prediction accuracy of the TSC-LSTM model were less than 2 mm
achieving a range of 1.2426 to 1.7403 mm. (3) Single-point prediction results demonstrate the TSC-LSTM model's superior ability to accurately capture local changes in the cumulative settlement data. The TSC-LSTM model proposed in this study outperforms the traditional LSTM model in terms of prediction accuracy and model stability
thereby providing robust support for in-depth research on urban land subsidence.
关键词
地面沉降预测TSC-LSTM模型SBAS-InSAR累积沉降数据分解新密市LSTM
Keywords
Ground settlement predictionTSC-LSTMSBAS-InSARCumulative settlement data decompositionXinmi CityLSTM
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