Abstract
Abstract. With the rapid growth of data volume and the development of artificial intelligence technology, deep-learning methods are a new way to model land subsidence. We utilized a long short-term memory (LSTM) model, a deep-learning-based time-series processing method to model the land subsidence under multiple influencing factors. Land subsidence has non-linear and time dependency characteristics, which the LSTM model takes into account. This paper modelled the time variation in land subsidence for 38 months from 2011 to 2015. The input variables included the change in land subsidence detected by InSAR technology, the change in confined groundwater level, the thickness of the compressible layer and the permeability coefficient. The results show that the LSTM model performed well in areas where the subsidence is slight but poorly in places with severe subsidence.
Highlights
The continuous over-pumping of groundwater can result in dramatic drawdown and regional land subsidence, threatening the living environment
Some researchers proposed the modified grey model (GM) model combined with artificial neural network (ANN) or other algorithms to deal with the non-linear features (Li et al, 2007)
To evaluate the impact of land subsidence severity on the model results, we chose P11 and P12 located in the southern area where the subsidence is small, P4 and P10 at the edges of the subsidence regions and P1 and P7 in the severe land subsidence areas
Summary
The continuous over-pumping of groundwater can result in dramatic drawdown and regional land subsidence, threatening the living environment. Land subsidence is a complex process influenced by the interaction of anthropogenic activities and the hydrogeological environment It often develops unevenly and seasonally and can display hysteresis depending on the soil mechanical properties (Ezquerro et al, 2014; Miller and Shirzaei, 2015; Bonì et al, 2016; Gao et al, 2018; Haghighi and Motagh, 2019). Some researchers proposed the modified GM model combined with artificial neural network (ANN) or other algorithms to deal with the non-linear features (Li et al, 2007) These methods can have a good short-term prediction and perform well when data volume is small, while the deep information cannot be mined when the data volume is large and cannot be used in long-term prediction
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