Spatiotemporal deformation characteristics and triggering factors of Baijiabao landslide in Three Gorges Reservoir region, China

Abstract Variations of reservoir water level and seasonal precipitation have resulted in significant movement and destabilization of landslides in the Three Gorges Reservoir (TGR) region of China since reservoir impoundment in 2003. An example is the Baijiabao landslide, a large, actively creeping landslide located in the steep lower valley of the Xiangxi River, about 55 km upstream of the TGR dam in the Yangtze River. Twelve years of monthly monitoring at four GPS stations and routine, monthly field observations show cumulative GPS displacements as large as >1.5 m and widely developed ground cracks. GPS monitoring results show that most movement takes place in rapid steps that coincide with the rainy season and the period of annual reservoir drawdown, with particularly large steps in 2009, 2012 and 2015. This step-like pattern of displacement is also shown by daily data from an automatic monitoring system installed in 2017. The total period of acceleration shown by these daily data was about six weeks long, with rapid movement starting during rapid reservoir drawdown, and terminating when the reservoir began to rise again. In particular, most of the 2018 displacement occurred in only two weeks. Different subzones of the landslide move at different rates and exhibit different features of deformation. The neighborhood rough set theory is used to identify the triggering factors responsible for landslide deformation. The most important triggering factors vary between different sites, data types and the time interval used to define them. The surface deformation and ground crack widening are controlled by the combination of rainfall and variations in the reservoir water levels, whereas the deformation of the sliding zone is most sensitive to the latter. The results show that daily data are needed to capture important, short-term landslide responses. The neighborhood rough set theory for determination of triggering factors is suggested for deformation prediction, stability evaluation, and prevention and control of reservoir landslides in this and other regions.

[1]  Robert E. Criss,et al.  Identification of movement characteristics and causal factors of the Shuping landslide based on monitored displacements , 2018, Bulletin of Engineering Geology and the Environment.

[2]  Erik Eberhardt,et al.  Kinematic behaviour and velocity characteristics of a complex deep-seated crystalline rockslide system in relation to its interaction with a dam reservoir , 2010 .

[3]  Paolo Paronuzzi,et al.  Influence of filling–drawdown cycles of the Vajont reservoir on Mt. Toc slope stability , 2013 .

[4]  Da Huang,et al.  Influence of filling-drawdown cycles of the Three Gorges reservoir on deformation and failure behaviors of anaclinal rock slopes in the Wu Gorge , 2017 .

[5]  Jiang Yunliang,et al.  Quick attribute reduct algorithm for neighborhood rough set model , 2014 .

[6]  Changdong Li,et al.  A preliminary study on the location of the stabilizing piles for colluvial landslides with interbedding hard and soft bedrocks , 2017 .

[7]  Chien-Chung Chan,et al.  Combined rough sets with flow graph and formal concept analysis for business aviation decision-making , 2011, Journal of Intelligent Information Systems.

[8]  Li Luo,et al.  Landslide Monitoring Network Establishment within Unified Datum and Stability Analysis in the Three Gorges Reservoir Area , 2017, J. Sensors.

[9]  Biswajeet Pradhan,et al.  A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping , 2014, Landslides.

[10]  Jordi Corominas,et al.  Canelles landslide: modelling rapid drawdown and fast potential sliding , 2012, Landslides.

[11]  R. Cojean,et al.  Analysis and modeling of slope stability in the Three-Gorges Dam reservoir (China) — The case of Huangtupo landslide , 2011 .

[12]  Xin-li Hu,et al.  Field monitoring and deformation characteristics of a landslide with piles in the Three Gorges Reservoir area , 2018, Landslides.

[13]  S. Loew,et al.  Detailed hydrogeological analysis of a deep-seated rockslide at the Gepatsch reservoir (Klasgarten, Austria) , 2016, Hydrogeology Journal.

[14]  Andrzej Skowron,et al.  Rough sets: Some extensions , 2007, Inf. Sci..

[15]  H. Zheng,et al.  Huangtupo landslide stability under water level fluctuations of the Three Gorges reservoir , 2016, Landslides.

[16]  Kaixiang Zhang,et al.  Application of a two-step cluster analysis and the Apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China , 2016, Environmental Earth Sciences.

[17]  Andrew K. C. Wong,et al.  Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Qinglin Yi,et al.  Landslide deformation behavior influenced by water level fluctuations of the Three Gorges Reservoir (China) , 2018, Engineering Geology.

[19]  Junwei Ma,et al.  Identification of causal factors for the Majiagou landslide using modern data mining methods , 2017, Landslides.

[20]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[21]  C. Anagnostopoulou,et al.  A study of weather types at Athens and Thessaloniki and their relationship to circulation types for the cold-wet period, part II: discriminant analysis , 2009 .

[22]  Qinghua Hu,et al.  Neighborhood rough set based heterogeneous feature subset selection , 2008, Inf. Sci..

[23]  Ailan Che,et al.  Establishing a monitoring network for an impoundment-induced landslide in Three Gorges Reservoir Area, China , 2009 .

[24]  Mihai Micu,et al.  A deep-seated landslide dam in the Siriu Reservoir (Curvature Carpathians, Romania) , 2013, Landslides.

[25]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[26]  C. Anagnostopoulou,et al.  A study of weather types at Athens and Thessaloniki and their relationship to circulation types for the cold-wet period, part I: two-step cluster analysis , 2009 .

[27]  Huan Liu,et al.  Consistency-based search in feature selection , 2003, Artif. Intell..

[28]  Thomas Glade,et al.  Landslide Hazard and Risk: Issues, Concepts and Approach , 2012 .

[29]  Robert E. Criss,et al.  Evolution characteristics of the Huangtupo landslide based on in situ tunneling and monitoring , 2015, Landslides.

[30]  Qinghua Hu,et al.  Neighborhood classifiers , 2008, Expert Syst. Appl..

[31]  C. Kilburn,et al.  Forecasting giant, catastrophic slope collapse: lessons from Vajont, Northern Italy , 2003 .

[32]  Manhua Liu,et al.  Fingerprint orientation field reconstruction by weighted discrete cosine transform , 2014, Inf. Sci..

[33]  Zhitao Huo,et al.  The July 14, 2003 Qianjiangping landslide, Three Gorges Reservoir, China , 2004 .

[34]  Piotr Synak,et al.  Infobright Analytic Database Engine Using Rough Sets and Granular Computing , 2010, 2010 IEEE International Conference on Granular Computing.

[35]  M. Rossi,et al.  The rainfall intensity–duration control of shallow landslides and debris flows: an update , 2008 .

[36]  Hongde Wang,et al.  Real-time monitoring and early warning of landslides at relocated Wushan Town, the Three Gorges Reservoir, China , 2010 .

[37]  Peng Liu,et al.  Spatiotemporal characteristics of the Huangtupo landslide in the Three Gorges region (China) constrained by radar interferometry , 2014 .

[38]  Qiang Shen,et al.  Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches , 2004, IEEE Transactions on Knowledge and Data Engineering.

[39]  Ruiqing Niu,et al.  Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China , 2018, Comput. Geosci..

[40]  Filippo Catani,et al.  Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method , 2018, Landslides.