State fusion entropy for continuous and site-specific analysis of landslide stability changing regularities

Liu, Yong; Qin, Zhimeng; Hu, Baodan; Feng, Shuai

doi:10.5194/nhess-18-1187-2018

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…The SCM is invented under this context of random and fuzzy features of the dam break risk system. It describes the concept of clouds, reflects the randomness and fuzziness of concepts in natural language, and realizes the conversion between qualitative and quantitative information (D. Liu et al, 2018). In the process of group decision-making, the traditional method is only a simple algebraic operation of an expert's ratings, which could not reflect the disagreements of different experts and the concentration of opinions.…”

Section: Weight-calculating Model Based On Scm-improved Entropy Methodsmentioning

confidence: 99%

“…The analytic hierarchy process (AHP) is faced with the difficulty of consistency checking when dealing with the conditions of multiple factors (more than nine) (Su et al, 2016). When previous studies used the data of SCM to calculate weights, they had neglected the entropy when applying the SCM to convert subjective opinions, resulting in the imperfection of information utilization (Mithas et al, 2011;Wan et al, 2015). These mentioned defects all lead to lack of scientificity in the calculation of weight.…”

Section: Introductionmentioning

confidence: 99%

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Weight analysis of influencing factors of dam break risk consequences

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. There are numerous influencing factors of the risk consequences of dam break. The scientific and reasonable index system and its weight distribution are some of the key elements for comprehensive evaluation of the dam break risk. Taking into consideration 20 factors, including hazards, exposure and vulnerability, the evaluation index system of the consequences of dam break risk is constructed. Using the Statistical Cloud Model (SCM) to improve the entropy method, we establish the weight calculation model of the influencing factors of dam break risk consequences. The results shows that the top five factors with the highest weight are risk population, flood intensity, alert time, risk understanding and distance from the dam. Compared to traditional algebraic weight calculation methods, the result is basically consistent with the algebraic weight distribution, and increases the range by 2.03 times, supporting a more scientific basis for recognizing and evaluating dam break risk consequences.

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Section: Weight-calculating Model Based On Scm-improved Entropy Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weight analysis of influencing factors of dam break risk consequences

2018

Nat. Hazards Earth Syst. Sci.

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“…Correspondingly, the primary objective of landslide prediction in quantitative terms is to simulate and predict landslide displacement data using prediction models, establish threshold criteria, and ultimately achieve accurate landslide forecasting (Kirschbaum et al, 2010;Fan et al, 2019a;Yang et al, 2019;Guzzetti et al, 2020;Zhang et al, 2022c). The landslide displacement prediction is a key component of early warning systems that emphasizes the ease of access, quantification, and reliability of displacement monitoring data (Du et al, 2013;Manconi and Giordan, 2015;Liu et al, 2018;Yan et al, 2021). Global Navigation Satellite System (GNSS) technology is an effective and direct method in landslide evolution analysis, which can be used to monitor the surface displacement of landslides (Lian et al, 2014;Li et al, 2017;Huang et al, 2022).…”

Section: Parameters For Landslide Predictionmentioning

confidence: 99%

Landslide displacement prediction from on-site deformation data based on time series ARIMA model

Wang,

Tang,

Hou

et al. 2023

Front. Environ. Sci.

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Time series Autoregressive Integrated Moving Average (ARIMA) model is often used in landslide prediction and forecasting. However, few conditions have been suggested for the application of ARIMA models in landslide displacement prediction. This paper summarizes the distribution law of the tangential angle in different time periods and analyzes the landslide displacement data by combining wavelet transform. It proposes an applicable condition for the ARIMA model in the field of landslide prediction: when the landslide deformation is in the initial deformation to initial acceleration stage, i.e., the tangential angle of landslide displacement is less than 80°, the ARIMA model has higher prediction accuracy for 24-h landslide displacement data. The prediction results are RMSE = 4.52 mm and MAPE = 2.39%, and the prediction error increases gradually with time. Meanwhile, the ARIMA model was used to predict the 24-h displacements from initial deformation to initial acceleration deformation for the landslide in Guangna Township and the landslide in Libian Gully, and the prediction results were RMSE = 1.24 mm, MAPE = 1.34% and RMSE = 5.43 mm, MAPE = 1.67%, which still maintained high accuracy and thus verified this applicable condition. At the same time, taking the landslide of Libian Gully as an example, the ARIMA model was used to test the displacement prediction effect of the landslide in the Medium-term acceleration stage and the Imminent sliding stage (the tangential angle of landslide displacement is 80° and 85°, respectively). The relative error of displacement data prediction in the Medium-term acceleration stage is within 3%, while the relative error of the prediction value in the Imminent sliding stage is more than 3%, and the error gradually increases with time. This demonstrates that the relative error of the ARIMA model in landslide prediction and forecasting is within 3%. The relative error of the prediction value in the Imminent sliding stage is above 3%, and the error increases gradually with time. Meanwhile, the prediction results are analyzed and it is concluded that the increase in prediction time and tangential angles are the main reasons for the increase in error. The applicable conditions proposed in this study can provide a reference for the application of ARIMA model in landslide prediction and forecast.

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“…A variety of data-driven models have had significant developments in recent years (Kayacan et al 2010;Liu et al 2014;Zhou et al 2016;Liu et al 2018;Reichenbach et al 2018). Among data-driven models, machine learning (ML) methods are widely applied for landslide displacement predictions and have achieved good performances (Zhou et al, 2018a).…”

Section: Introductionmentioning

confidence: 99%

Time series analysis and long short-term memory neural network to predict landslide displacement

et al. 2019

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A good prediction of landslide displacement is an essential component for implementing an early warning system. In the Three Gorges Reservoir Area (TGRA), many landslides deform distinctly and in steps from April to September each year under the influence of seasonal rainfall and periodic fluctuation in reservoir water level. The sliding becomes more uniform again from October to April. This landslide deformation pattern leads to accumulated displacement versus time showing a step-wise curve. Most of the existing predictive models express static relationships only. However, the evolution of a landslide is a complex nonlinear dynamic process. This paper proposes a dynamic model to predict landslide displacement, based on time series analysis and long short-term memory neural network (LSTM). The accumulated displacement was decomposed into a trend term and a periodic term in the time series analysis. A cubic polynomial function was selected to predict the trend displacement. By analyzing the relationships between landslide deformation, rainfall and reservoir water level, a LSTM model was used to predict the periodic displacement. The LSTM approach was found to more properly model the dynamic characteristics of landslides than static models, and make full use of the historical information. The performance of the model was validated with the observations of two step-wise landslides in the TGRA, the Baishuihe landslide and Bazimen landslide. The application of the model to those two landslides demonstrates that the LSTM model provides a good representation of the measured displacements and gives a more reliable prediction of landslide displacement than the static support vector machine (SVM) model. It is concluded that the proposed model can be used to effectively predict the displacement of step-wise landslides in the TGRA.

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State fusion entropy for continuous and site-specific analysis of landslide stability changing regularities

Cited by 7 publications

References 32 publications

Weight analysis of influencing factors of dam break risk consequences

Weight analysis of influencing factors of dam break risk consequences

Landslide displacement prediction from on-site deformation data based on time series ARIMA model

Time series analysis and long short-term memory neural network to predict landslide displacement

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