Landslide displacement prediction based on time series analysis and data assimilation with hydrological factors
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ORIGINAL PAPER
Landslide displacement prediction based on time series analysis and data assimilation with hydrological factors Jing Wang 1 & Guigen Nie 1,2 & Changhu Xue 1 Received: 23 January 2019 / Accepted: 17 May 2020 # Saudi Society for Geosciences 2020
Abstract The displacement prediction of an active landslide is a complicated and challenging problem worldwide. Currently, most prediction experiments focus on the mechanism model and fail to integrate with the influence factors. In this paper, a method of landslide data assimilation is proposed to predict the landslide displacement, and real data tests are carried out to support the theoretical calculation. The obtained results show better performance of the proposed method compared with the general method. Data assimilation shows a relatively 40.32% improve in RMSE. This study can strongly confirm our proposed method presents a superior quality, improves the accuracy of landslide deformation prediction. And it is expected to be significant for the landslide displacement prediction in the future. Keywords Landslide . Time series analysis . Data assimilation . Particle filter
Introduction Landslides are typical geological disasters which endanger personal and property safety every year in the world (Runqiu 2009; Mahdadi et al. 2018; Schuster and Highland 2001; Thiebes et al. 2014; Hungr et al. 2014; Ardiclioglu and Kuriqi 2019). The stability of landslide could be deduced effectively by displacement prediction (Corominas et al. 2005; Huang et al. 2017). Therefore, the landslide displacement prediction is a practical approach to provide early warnings (Casagli et al. 2010; Intrieri et al. 2013; Miao et al. 2017). The prediction of landslide deformation is a complicated nonlinear system problem (Liu et al. 2014). Since the 1960s, methods of the landslide displacement prediction have been developed from the numerical model to the synthetical forecasting system (Qiang et al. 2004). The synthetical system can make use of all kinds of measuring method. So it attracts more Responsible Editor: Zhen-Dong Cui * Guigen Nie [email protected] 1
GNSS Research Center, Wuhan University, Wuhan 430079, Hubei, China
2
Collaborative Innovation Center for Geospatial Information Technology, Wuhan 430079, Hubei, China
and more research interests in recent years. However, lacking prediction theory at present increases the difficulty (Frattini and Crosta 2013). Moreover, the uncertainty of landslides factors makes it hard to forecast landslide in time. And model parameters influence the performance of system evidently (Yin and Yu 2007; Sun et al. 2008). In this regard, it is important to advanced hydrometeorological system to better predict the hydrometeorological process that influences landslides. (Kuriqi 2016; Kuriqi et al. 2020) To work around these issues, a data assimilation (DA) methodology is applied here (Daley 1991; Talagrand 1997). The DA is a method that integrates the new observation data in the dynamic operation of the numerical model on the basis of the spatial and
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