750 research outputs found
Assessment of earthquake-induced slope deformation of earth dams using soft computing techniques
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Evaluating behavior of earth dams under dynamic loads is one of the most important problems associated with the initial design of such massive structures. This study focuses on prediction of deformation of earth dams due to earthquake shaking. A total number of 103 real cases of deformation in earth dams due to earthquakes that has occurred over the past years were gathered and analyzed. Using soft computing methods, including feed-forward back-propagation and radial basis function based neural networks, two models were developed to predict slope deformations in earth dams under variant earthquake shaking. Earthquake magnitude (M w ), yield acceleration ratio (a y /a max ), and fundamental period ratio (T d /T p ) were considered as the most important factors contributing to the level of deformation in earth dams. Subsequently, a sensitivity analysis was conducted to assess the performance of the proposed model under various conditions. Finally, the accuracy of the developed soft computing model was compared with the conventional relationships and models to estimate seismic deformations of earth dams. The results demonstrate that the developed neural model can provide accurate predictions in comparison to the available practical charts and recommendations
A Novel Method for Landslide Displacement Prediction by Integrating Advanced Computational Intelligence Algorithms
Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability
Advanced Bayesian networks for reliability and risk analysis in geotechnical engineering
The stability and deformation problems of soil have been a research topic of great
concern since the past decades. The potential catastrophic events are induced by various complex factors, such as uncertain geotechnical conditions, external environment, and anthropogenic influence, etc. To prevent the occurrence of disasters in geotechnical engineering, the main purpose of this study is to enhance the Bayesian networks (BNs) model for quantifying the uncertainty and predicting the risk level in solving the geotechnical problems. The advanced BNs model is effective for analyzing the geotechnical problems in the poor data environment. The advanced BNs approach proposed in this study is applied to solve the stability of soil slopes problem associated with the specific-site data. When probabilistic models for soil properties are adopted, enhanced BNs approach was adopted to cope with continuous input parameters. On the other hand, Credal networks (CNs), developed on the basis of BNs, are specially used for incomplete input information. In addition, the probabilities of slope failure are also investigated for different evidences. A discretization approach for the enhanced BNs is applied in the case of evidence entering into the continuous nodes. Two examples implemented are to demonstrate the feasibility and predictive effectiveness of the BNs model. The results indicate the enhanced BNs show a precisely low risk for the slope studied. Unlike the BNs, the results of CNs are presented with bounds. The comparison
of three different input information reveals the more imprecision in input, the more uncertainty in output. Both of them can provide the useful disaster-induced information
for decision-makers. According to the information updating in the models, the position
of the water table shows a significant role in the slope failure, which is controlled by
the drainage states. Also, it discusses how the different types of BNs contribute to
assessing the reliability and risk of real slopes, and how new information could be
introduced in the analysis. The proposed models in this study illustrate the advanced
BN model is a good diagnosis tool for estimating the risk level of the slope failure.
In a follow-up study, the BNs model is developed based on its potential capability
for the information updating and importance measure. To reduce the influence of
uncertainty, with the proposed BN model, the soil parameters are updated accurately
during the excavation process, and besides, the contribution of epistemic uncertainty from geotechnical parameters to the potential disaster can be characterized based on the developed BN model. The results of this study indicate the BNs model is an
effective and flexible tool for risk analysis and decision making support in geotechnical engineering
Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA
An Application of Artificial Neural Network (ANN) for Landslide Hazard Mapping, Susceptibility and Early Warning System: A Review
An Application of ANN for Landslide Early Warning System in Darjeeling hill regio
Landslide displacement forecasting using deep learning and monitoring data across selected sites
Accurate early warning systems for landslides are a reliable
risk-reduction strategy that may significantly reduce fatalities
and economic losses. Several machine learning methods have
been examined for this purpose, underlying deep learning (DL)
models’ remarkable prediction capabilities. The long short-term
memory (LSTM) and gated recurrent unit (GRU) algorithms are
the sole DL model studied in the extant comparisons. However,
several other DL algorithms are suitable for time series forecasting
tasks. In this paper, we assess, compare, and describe seven DL
methods for forecasting future landslide displacement: multi-layer
perception (MLP), LSTM, GRU, 1D convolutional neural network
(1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture
composed of 1D CNN and LSTM (Conv-LSTM). The investigation
focuses on four landslides with different geographic locations,
geological settings, time step dimensions, and measurement
instruments. Two landslides are located in an artificial reservoir
context, while the displacement of the other two is influenced just
by rainfall. The results reveal that the MLP, GRU, and LSTM models
can make reliable predictions in all four scenarios, while the Conv-
LSTM model outperforms the others in the Baishuihe landslide,
where the landslide is highly seasonal. No evident performance
differences were found for landslides inside artificial reservoirs
rather than outside. Furthermore, the research shows that MLP is
better adapted to forecast the highest displacement peaks, while
LSTM and GRU are better suited to model lower displacement
peaks. We believe the findings of this research will serve as a precious
aid when implementing a DL-based landslide early warning
system (LEWS).SUPPORTO
SCIENTIFICO PER L’OTTIMIZZAZIONE, IMPLEMENTAZIONE E
GESTIONE DEL SISTEMA DI MONITORAGGIO CON AGGIORNAMENTO
DELLE SOGLIE DI ALLERTAMENTO DEL FENOMENO
FRANOSO DI SANT’ANDREA – PERAROLO DI CADORE (BL)”
and the Spanish Grant “SARAI, PID2020-116540RB-C21,MCIN/AEI/10.13039/501100011033” and “RISKCOASTInSAR displacement data of the El Arrecife landslideGeohazard Exploitation Platform (GEP) of the European
Space AgencyNoR Projects Sponsorship
(Project ID: 63737
Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. An accurate inventory map is a prerequisite for the analysis of landslide susceptibility, hazard, and risk. Field survey, optical remote sensing, and synthetic aperture radar techniques are traditional techniques for landslide detection in tropical regions. However, such techniques are time consuming and costly. In addition, the dense vegetation of tropical forests complicates the generation of an accurate landslide inventory map for these regions. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) has been used to generate accurate landslide maps. This study proposes the use of recurrent neural networks (RNN) and multi-layer perceptron neural networks (MLP-NN) in landscape detection. These efficient neural architectures require little or no prior knowledge compared with traditional classification methods. The proposed methods were tested in the Cameron Highlands, Malaysia. Segmentation parameters and feature selection were respectively optimized using a supervised approach and correlation-based feature selection. The hyper-parameters of network architecture were defined based on a systematic grid search. The accuracies of the RNN and MLP-NN models in the analysis area were 83.33% and 78.38%, respectively. The accuracies of the RNN and MLP-NN models in the test area were 81.11%, and 74.56%, respectively. These results indicated that the proposed models with optimized hyper-parameters produced the most accurate classification results. LiDAR-derived data, orthophotos, and textural features significantly affected the classification results. Therefore, the results indicated that the proposed methods have the potential to produce accurate and appropriate landslide inventory in tropical regions such as Malaysia
ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications
Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
Landslide Susceptibility Prediction Modeling Based on Self-Screening Deep Learning Model
Landslide susceptibility prediction has always been an important and
challenging content. However, there are some uncertain problems to be solved in
susceptibility modeling, such as the error of landslide samples and the complex
nonlinear relationship between environmental factors. A self-screening graph
convolutional network and long short-term memory network (SGCN-LSTM) is
proposed int this paper to overcome the above problems in landslide
susceptibility prediction. The SGCN-LSTM model has the advantages of wide width
and good learning ability. The landslide samples with large errors outside the
set threshold interval are eliminated by self-screening network, and the
nonlinear relationship between environmental factors can be extracted from both
spatial nodes and time series, so as to better simulate the nonlinear
relationship between environmental factors. The SGCN-LSTM model was applied to
landslide susceptibility prediction in Anyuan County, Jiangxi Province, China,
and compared with Cascade-parallel Long Short-Term Memory and Conditional
Random Fields (CPLSTM-CRF), Random Forest (RF), Support Vector Machine (SVM),
Stochastic Gradient Descent (SGD) and Logistic Regression (LR) models.The
landslide prediction experiment in Anyuan County showed that the total accuracy
and AUC of SGCN-LSTM model were the highest among the six models, and the total
accuracy reached 92.38 %, which was 5.88%, 12.44%, 19.65%, 19.92% and 20.34%
higher than those of CPLSTM-CRF, RF, SVM, SGD and LR models, respectively. The
AUC value reached 0.9782, which was 0.0305,0.0532,0.1875,0.1909 and 0.1829
higher than the other five models, respectively. In conclusion, compared with
some existing traditional machine learning, the SGCN-LSTM model proposed in
this paper has higher landslide prediction accuracy and better robustness, and
has a good application prospect in the LSP field
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