Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models

Li, Huajin; Liu, Fangzhou; He, Yusen; Deng, Jiahao

doi:10.1007/s10346-018-1020-2

Cited by 95 publications

(54 citation statements)

References 38 publications

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“…What is known is that many geological and topographic conditions can influence the occurrence of landslides, such as formation lithology, faults, hydrology, attitude, slope angle, soil, and vegetation [8,9]. As landslide susceptibility mapping is the first and foremost step in landslide prevention, numerous researchers have been devoted to landslide susceptibility mapping in past years [10][11][12][13][14]. In general, the methods used in previous studies can be roughly divided into two types: qualitative and quantitative, for example, analytic hierarchy process (AHP) is the most commonly-used qualitative approach in landslide susceptibility mapping [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.Symmetry 2019, 11, 762 2 of 24years, machine learning method has been gradually applied in landslide susceptibility mapping researches, such as artificial neural network (ANN) [17][18][19], support vector machine (SVM) [20][21][22], logistic model tree (LMT) [23,24], rotation forest (RF) [25,26], classification and regression tree (CART) [27,28], adaptive neuro-fuzzy inference systems (ANFIS) [29,30], and genetic algorithm (GA) [31,32]. Furthermore, statistical approach is another widely-used model which can also be divided into two types: bivariate and multivariate. In statistical approaches, the weight of each class of every factor was calculated by overlying the landslide inventory map and landslide conditioning factors map [33,34]. The frequently-used statistical approaches are frequency ratio (FR) [35][36][37], logistic regression (LR), evidential belief function (EBF) [38,39], weights of evidence (WoE) [40,41], certainty factor (CF) [42,43], and information value (IV) [44,45].In this paper, three bivariate statistical methods, namely weight of evidence (WoE), evidence belief function (EBF), index of entropy (IoE), coupled with logistic regression (LR) were introduced to carry out the landslide susceptibility mapping in Muchuan County, which lies in the southeast of Sichuan Province, China. All the approaches were first applied in Muchuan County and the main purpose of this article is to obtain the landslide susceptibility maps through the above models. The main difference between this study and literatures referenced above is to achieve accurate landslide susceptibility maps for...

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Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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“…Xie et al [104] Laowuji, China Rainfall, toe excavation Total Station LSTM Bossi and Marcato [105] Passo della Morte, Italy Rainfall, groundwater Inclinometer Linear regression Yang et al [106] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS LSTM Miao et al [107] Baishuihe, China Rainfall, reservoir level GNSS, inclinometer GA-SVR, GS-SVR, PSO-SVR Li et al [37] Baishuihe, China Rainfall, reservoir level GNSS LASSO-ELM, Copula (ELM, SVM, RF, kNN) Logar et al [108] Ventor, United Kingdom Rainfall Crackmeter ANN Krkač et al [33] Kostanjek, Croatia Groundwater (change), season GNSS RF Zhou et al [109] Bazimen, China Rainfall, reservoir level GNSS PSO-SVM (GA-SVM, GS-SVM, BPNN) Cao et al [110] Baijiabao, China Rainfall, groundwater, reservoir level GNSS ELM (SVM) Lian et al [111] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS LSSVM, ELM, combination Chen and Zeng [112] Baishuihe, China None GNSS BPNN Du et al [31] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS, inclinometer BPNN Lian et al [113] Buishuihe, China None GNSS EEMD-ELM, M-EEMD-ELM (ANN, BPNN, RBFNN, SVR, ELM) Corominas et al [114] Vallcebre, Spain Groundwater Extensometers Physics Neaupane and Achet [115] Okharpauwa, Nepal…”

Section: Methods (Reference Methods)mentioning

confidence: 99%

“…Toe erosion-either gradual by water or sudden by building activity-is often captured in land use. However, local situations may require extra variables to be added, such as reservoir water level [37], or ground temperature for freeze-thaw effects. The systematic, often global, availability of satellite remote sensing data sources is a valuable addition to local (field) surveys and monitoring, where data availability is dependent on commissioning by local authorities.…”

Section: Monitoring Opportunities For Slow-moving Deep-seated Landslidesmentioning

confidence: 99%

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Natijne

Lindenbergh

Bogaard

2020

Sensors

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Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.

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“…Dong and Li (2012) proposed a model that coupled the Gray method and general regression neural networks (GM-GRNN) and applied it to the prediction of sliding deformation of the Dahu landslide. Li and Kong (2014) carried out a genetic algorithm and support vector machine (GA-SVM) method to establish a mathematical function prediction model. Although the above methods have certain practicability in the prediction of landslides, it is still problematic to carry out forecasts of rainfall-induced landslides in real time (Yin et al, 2010) -for the reason that surveillance photographs or optical remote-sensing satellites are not immediately available (Lee et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation

Wang

Nie

Gao

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, and applying a classical prediction method will result in significant error. The data assimilation method offers a new way to merge multisource data with the model. However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide systems. In this paper, simultaneous state and parameter estimation (SSPE) using particle-filter-based data assimilation is applied to predict displacement of the landslide. A landslide SSPE assimilation strategy can make use of time-series displacements and hydrological information for the joint estimation of landslide displacement and model parameters, which can improve the performance considerably. We select Xishan Village, Sichuan Province, China, as the experiment site to test the SSPE assimilation strategy. Based on the comparison of actual monitoring data with prediction values, results strongly suggest the effectiveness and feasibility of the SSPE assimilation strategy in short-term landslide displacement estimation.

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Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models

Cited by 95 publications

References 38 publications

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation

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