Urban surface deformation monitoring and prediction by integrating SBAS-InSAR and Elman neural network

References

• Berardino, P., et al., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE transactions on geoscience and remote sensing, 40 (11), 2375–2383.
   Web of Science ®Google Scholar
• Chen, M., et al., 2016. Imaging land subsidence induced by groundwater extraction in Beijing (China) using satellite radar interferometry. Remote sensing, 8 (6), 468.
   Web of Science ®Google Scholar
• Chen, Y., et al., 2021. Prediction of InSAR deformation time-series using a long short-term memory neural network. International journal of remote sensing, 42 (18), 6919–6942.
   Web of Science ®Google Scholar
• Deng, Z., et al., 2017. Land subsidence prediction in Beijing based on PS-InSAR technique and improved Grey-Markov model. GIScience & remote sensing, 54 (6), 797–818.
   Web of Science ®Google Scholar
• Ding, Q., et al., 2021. Monitoring, analyzing and predicting urban surface subsidence: a case study of Wuhan City, China. International journal of applied earth observation and geoinformation, 102 (6).
   Google Scholar
• Eusuff, M.M. and Lansey, K.E., 2003. Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of water resources planning and management, 129 (3), 210–225.
   Web of Science ®Google Scholar
• Estelle, C., et al., 2014. Land subsidence in central Mexico detected by ALOS InSAR time-series. Remote sensing of environment, 140, 94–106.
   Web of Science ®Google Scholar
• Gama, F.F., et al., 2020. Deformations prior to the Brumadinho Dam collapse revealed by Sentinel-1 InSAR data using SBAS and PSI techniques. Remote sensing, 12 (21), 3664.
   Web of Science ®Google Scholar
• Hooper, A., et al., 2004. A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophysical research letters, 31 (23).
   Web of Science ®Google Scholar
• Hooper, A., Segall, P., and Zebker, H., 2007. Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcán Alcedo, Galápagos. Journal of geophysical research atmospheres, 112 (B7).
   Web of Science ®Google Scholar
• Lanari, R., et al., 2004. Satellite radar interferometry time series analysis of surface deformation for Los Angeles, California. Geophysical research letters, 31 (23).
   Web of Science ®Google Scholar
• Liu, H., Mi, X., and Li, Y., 2018. Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy conversion and management, 156, 498–514.
   Web of Science ®Google Scholar
• Li, Y., et al., 2020. Examining land use/land cover change and the summertime surface urban heat island effect in fast-growing Greater Hefei, China: implications for sustainable land development. ISPRS international journal of geo-information, 9 (10), 568.
   Web of Science ®Google Scholar
• Miranda, S.F., et al., 2020. Evolution assessment of structurally-controlled differential subsidence using SBAS and PS interferometry in an emblematic case in Central Mexico. Engineering geology, 279, 105860.
   Web of Science ®Google Scholar
• Ren, M., et al., 2022. A prediction model for surface deformation caused by underground mining based on spatio-temporal associations. Geomatics, natural hazards and risk, 13 (1), 94–122.
   Web of Science ®Google Scholar
• Tang, Y., et al., 2008. Application of grey theory-based model to prediction of land subsidence due to engineering environment in Shanghai. Environmental geology, 55 (3), 583–593.
   Web of Science ®Google Scholar
• Tao, Q., et al., 2021. SBAS-InSAR time series ground subsidence monitoring along Metro Line 13 in Qingdao, China. Arabian journal of geosciences, 14 (23), 1–14.
   Google Scholar
• Wang, L., et al., 2018. Research on probability integration parameter inversion of mining-induced surface subsidence based on quantum annealing. Environmental earth sciences, 77 (21), 740.1–740.13.
   Web of Science ®Google Scholar
• Wang, C., et al., 2021. Landslide displacement prediction method based on GA-Elman model. Applied sciences, 11 (22), 11030.
   Google Scholar
• Xu, Y., et al., 2008. The state of land subsidence and prediction approaches due to groundwater withdrawal in China. Natural hazards, 45, 123–135.
   Web of Science ®Google Scholar
• Xu, H., Liu, B., and Fang, Z., 2014. New grey prediction model and its application in forecasting land subsidence in coal mine. Natural hazards, 71 (2), 1181–1194.
   Web of Science ®Google Scholar
• Yu, L., et al., 2013. Estimation of calorific value of coals using BP and Elman neural networks. Advanced materials research, 781–784, 39–44.
   Google Scholar
• Zhang, Y., and Zhang, Y., 2013. Land subsidence prediction method of power cables pipe jacking based on the peck theory. Advanced materials research, 634–638, 3721–3724.
   Google Scholar
• Zhu, L., et al., 2013. Comprehensive analysis and artificial intelligent simulation of land subsidence of Beijing, China. Chinese geographical science, 23 (2), 237–248.
   Web of Science ®Google Scholar
• Zhu, L., et al., 2015. Land subsidence due to groundwater withdrawal in the northern Beijing plain, China. Engineering geology, 193 (2), 243–255.
   Web of Science ®Google Scholar
• Zhou, C., et al., 2016. Land subsidence under different land use in the eastern Beijing plain, China 2005–2013 revealed by InSAR timeseries analysis. GIScience & remote sensing, 53 (6), 671–688.
   Web of Science ®Google Scholar