Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory

Huang, Faming; Yin, Kunlong; Zhang, Guirong; Gui, Lei; Yang, Bo; Liu, Lei

doi:10.1007/s12665-016-6133-0

Cited by 92 publications

(42 citation statements)

References 57 publications

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“…Along with the development of information technologies, remote sensing and the geographic information system (GIS) have gradually become data sources and spatial analysis platforms for LSP [6,7]. Based on remote sensing and GIS, many mathematical models have been proposed to calculate landslide susceptibility indices (LSI), such as the analytic hierarchy process [8][9][10], weight evidence method [11], information value (IV) theory [5,12], frequency ratio (FR) method [13,14], logistic regression model [7,15,16], logistic tree model [17], random tree [18,19], boosted tree [20], multi-criteria evaluation model [21], artificial neural networks (ANNs) [22][23][24], support vector machine (SVM) [25][26][27], and neuro-fuzzy method [28]. Although many models have been proposed for LSP, there is no model that is universally accepted and there is much room for improvement for these models.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Huang

Yan

et al. 2019

Applied Sciences

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Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.

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Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Huang

Yan

et al. 2019

Applied Sciences

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“…South China is characterized by a subtropical humid monsoon climate with frequent heavy rainfall and exposure to sunlight. Hence, wetting-drying cycles, that is, alternating soil moisture saturation and water loss, can be used to effectively simulate the soil loss of the granite residual soil loss in the dry season and water absorption in the rainy season (Kalkan 2011;Chen and Ng 2013;Huang et al 2016aHuang et al , 2016b. Furthermore, South China is greatly affected by acid rain, which contributes to loss of rock components such as calcium and magnesium (Zhao et al 2018).…”

Section: Experimental Schemementioning

confidence: 99%

Experimental study on the disintegration of granite residual soil under the combined influence of wetting–drying cycles and acid rain

Liu

Song

Huang

2019

Geomatics, Natural Hazards and Risk

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Granite residual soil has obvious disintegration characteristics, resulting in serious water and soil losses in South China. There is a lack of studies on the disintegration of granite residual soil. Therefore, it is important to determine the disintegration characteristics of granite residual soil, especially under combined influence of wetting-drying cycles and acid rain. Granite residual soil from Jinqiao Village, Yudou County of South China, was used as experimental material. The disintegration velocity was evaluated to investigate the effects of wetting-drying cycles and acid rain on soil disintegration characteristics. Under the pH conditions of acid rain, disintegration velocity increases as the number of wetting-drying cycles increases (from 0 to 4), reaches a maximum after four wetting-drying cycles; then remains relatively constant as the wetting-drying cycle number increases from 4 to 7. Meanwhile, under conditions of a given wetting-drying cycle number, disintegration velocity increases with the decrease in pH from 7 to 4, reaches a maximum at a pH of 4, and then remains relatively constant when the pH decreases from 4 to 1. Moreover, the disintegration velocity under the combined influence of wetting-drying cycles and acid rain is considerably higher than that under individual factor.

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“…However, these machine learning models have disadvantages of local optimal values and over-fitting phenomena, which limit the prediction accuracies of CSA (Lee et al 2016;Huang et al 2016c). To overcome these disadvantages, a support vector machine (SVM), which has advantages such as excellent prediction performance and global optimum, is developed and proposed in this study (Cortes and Vapnik 1995).…”

Section: Introductionmentioning

confidence: 99%

Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

Sheng

Chai

et al. 2020

Geomatics, Natural Hazards and Risk

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Machine learning models are regarded as efficient and popular models for natural disaster susceptibility prediction. However, few studies have focussed on the applications of the latest popular machine learning models in collapse susceptibility assessment (CSA). This paper proposes a 3S (RS, GPS and GIS) technologybased support vector machine (SVM) to map collapse susceptibility in the Nantian area of China. The 3S technology-based backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) models are also proposed for comparison. First, 44 recorded collapses are identified through field investigation, and fourteen collapse-related causal factors are acquired using 3S technology. Second, among these recorded collapses and randomly selected 'non-collapses', 70% of the collapse and non-collapse grid cells are used to train the three models, while the remaining 30% are used to test the models. Third, the collapse susceptibility maps of the Nantian area are produced using the three models. Finally, the prediction accuracies of these models are evaluated. The results indicate that the SVM model has the highest prediction accuracy, while the RBFNN model has the lowest prediction accuracy for CSA. In addition, the distribution characteristics of collapse susceptibility in the Nantian area are produced well by all three models. ARTICLE HISTORY

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Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory

Cited by 92 publications

References 57 publications

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Experimental study on the disintegration of granite residual soil under the combined influence of wetting–drying cycles and acid rain

Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

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