Landslide displacement prediction technique using improved neuro-fuzzy system

Shihabudheen, K V; Peethambaran, Bipin

doi:10.1007/s12517-017-3278-4

Cited by 39 publications

(14 citation statements)

References 37 publications

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“…However, the series of landslide body displacements, which vary over time, can be used to describe the general laws of landslide development. Therefore, some researchers have proposed different methods for the prediction of landslide deformation and displacement [2,4,[7][8][9][10][11][12][13][14][15]. The methods can be roughly classified into four categories-deterministic methods [2,7,8,12], statistical methods [9,11], numerical simulations [4,10], and nonlinear methods [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021

Applied Sciences

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Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.

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Section: Introductionmentioning

confidence: 99%

Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021

Applied Sciences

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“…In CD models, some novel signal decomposition technologies, including wavelet transform (WT), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the original displacement time series into several displacement components (Lian et al, 2013;Huang et al, 2016;Shihabudheen and Peethambaran 2017;. It offers a practical solution to the incomplete decomposition problem of random displacement.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Hybrid Machine Learning Model For Forecasting The Step-Like Displacements of Reservoir Colluvial Landslides: A Case Study in The Three Gorges Reservoir Area, China

Miao

et al. 2021

Preprint

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Constructing an accurate and stable displacement prediction model is essential to build a capable early warning system for landslide disasters. To overcome the drawbacks of previous displacement prediction models for step-like landslides, such as the incomplete or excessive decompositions of cumulative displacements and input factors and the redundancy or lack of input factors, we propose an adaptive hybrid machine learning model. This model is composed of three parts. First, candidate factors are proposed based on the macroscopic deformation response of landslides. Then, the landslide displacement and its candidate factors are adaptively decomposed into different displacement and factor components by applying optimized variational mode decomposition (OVMD). Second, in the gray wolf optimizer-based kernel extreme learning machine (GWO-KELM) model, the global sensitivity analysis (GSA) of the prediction results of different displacement components to each decomposed factor is analyzed based on the PAWN method. Then, the decomposed factors are reduced according to the GSA results. Third, based on the reduced factors, the optimal GWO-KELM models of the different displacement components are established to predict the displacement. Taking the Baishuihe landslide as an example, we used the raw data of three representative monitoring sites from June 2006 to December 2016 to verify the validity, accuracy, and stability of the model. The results indicate that the proposed hybrid model can effectively determine the displacement decomposition parameters. In addition, this model performed well over a three-year forecast with low model complexity.

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“…Fang [21] applied the EEMD technique for analysis of the psychological state of investors in their study of the relationship between stock prices and investor psychology. Recently, an integrated approach using multiple models has been used for better performance in prediction problems [22][23][24][25][26][27][28][29][30]. For example, it was found that wind speed can be more accurately predicted by combining EMD and different prediction techniques [24].…”

Section: Introductionmentioning

confidence: 99%

Improving Deep Learning for Forecasting Accuracy in Financial Data

Lin

Huang

2020

Discrete Dynamics in Nature and Society

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Financial forecasting is based on the use of past and present financial information to make the best prediction of the future financial situation, to avoid high-risk situations, and to increase benefits. Such forecasts are of interest to anyone who wants to know the state of possible finances in the future, including investors and decision-makers. However, the complex nature of financial data makes it difficult to get accurate forecasts. Artificial intelligence, which has been shown to be suitable for analyzing very complex problems, can be applied to financial forecasting. Financial data is both nonlinear and nonstationary, with broadband frequency features. In other words, there is a large range of fluctuation, meaning that predictions made only using long short-term memory (LSTM) are not enough to ensure accuracy. This study uses an LSTM model for analysis of financial data, followed by a comparison of the analytical results with the actual data to see which has a larger root-mean-square-error (RMSE). The proposed method combines deep learning with empirical mode decomposition (EMD) to understand and predict financial trends from financial data. The financial data for this study are from the Taiwan corporate social responsibility (CSR) index. First, the EMD method is used to transform the CSR index data into a limited number of intrinsic mode functions (IMF). The bandwidth of these IMFs becomes narrower, with regular cyclic, periodic, or seasonal components in the time domain. In other words, the range of fluctuation is small. LSTM is a good way to forecast cyclic or seasonal data. The forecast result is obtained by adding all the IMFs together. It has been verified in past studies that only the LSTM and LSTM combined with the EMD can be used. The analytical results show that smaller RMSEs can be obtained using the LSTM combined with EMD compared to real data.

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Landslide displacement prediction technique using improved neuro-fuzzy system

Cited by 39 publications

References 37 publications

Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

Adaptive Hybrid Machine Learning Model For Forecasting The Step-Like Displacements of Reservoir Colluvial Landslides: A Case Study in The Three Gorges Reservoir Area, China

Improving Deep Learning for Forecasting Accuracy in Financial Data

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