scholarly journals Data Mining and Deep Learning for Predicting the Displacement of “Step-Like” Landslides

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 481
Author(s):  
Fasheng Miao ◽  
Xiaoxu Xie ◽  
Yiping Wu ◽  
Fancheng Zhao
Keyword(s):  
Data Mining ◽  
Water Levels ◽  
Three Gorges Reservoir Area ◽  
Data Mining Algorithm ◽  
Fruit Fly Optimization Algorithm ◽  
Reservoir Water ◽  
Triggering Factors ◽  
Prediction And Prevention ◽  
Landslide Displacement Prediction ◽  
Baishuihe Landslide

Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R2 values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters.

scholarly journals An Integrated Approach for Displacement Prediction of Landslides Based on the Data Mining and VMD-FOA-BPNN Model

2021 ◽  
Author(s):  
Fasheng Miao ◽  
Xiaoxu Xie ◽  
Yiping Wu ◽  
Linwei Li ◽  
Weiwei Zhan
Keyword(s):  
Data Mining ◽  
Prediction Models ◽  
Integrated Approach ◽  
Three Gorges Reservoir Area ◽  
Fruit Fly Optimization Algorithm ◽  
Reservoir Water ◽  
Displacement Prediction ◽  
Triggering Factors ◽  
Landslide Displacement Prediction ◽  
Combining Data

Abstract Landslide prediction is important for mitigating geohazards but is very challenging. The fluctuation of reservoir water level and rainfall are the main external triggering factors controlling the deformation of riverine landslides. In this paper, the Baishuihe landslide in the Three Gorges Reservoir area, which has a typical “step-like” behavior, is taken as the study case, and an integrated approach for landslide displacement prediction combining data mining and Variational Mode Decomposition, Fruit Fly Optimization Algorithm, Back Propagation Neural Network (VMD-FOA-BPNN) is proposed. Nine triggering factors including the reservoir level and rainfall are extracted. First, triggering factors and monthly velocity of the landslide are clustered into several categories by Two-step Clustering (TSC). Then, Apriori algorithm is used to mine the association rules between triggering factors and monthly velocity, and comprehensive contribution of each factor is calculated based on the data mining results. Next, the displacement of monitoring point ZG93 and triggering factors are decomposed by VMD based on the time series analysis of the landslide. Last, the trend term displacement is trained and predicted by the subsection functions, and FOA-BPNN models are used to train and predict the periodic and random term. The prediction results show that, compared with the current popular prediction models, the proposed model can effectively improve the prediction accuracy, which has high practicability and application value in the study of landslide displacement prediction.

Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods

2020 ◽  
Author(s):  
Fasheng Miao ◽  
Yiping Wu ◽  
Linwei Li ◽  
Kang Liao ◽  
Yang Xue
Keyword(s):  
Data Mining ◽  
Threshold Analysis ◽  
Triggering Factors ◽  
Mining Methods ◽  
Baishuihe Landslide

Time Series Prediction of Landslide Displacement Using SVM Model: Application to Baishuihe Landslide in Three Gorges Reservoir Area, China

2012 ◽  
Vol 239-240 ◽  
pp. 1413-1420 ◽  
Author(s):  
Chuan Hua Zhu ◽  
Guang Dao Hu
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Three Gorges Reservoir ◽  
Time Series Prediction ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Landslide Monitoring ◽  
Three Gorges ◽  
Reservoir Area ◽  
Baishuihe Landslide

Time series analysis has the ability to forecast the evolve trend of complex systems, which has been the issue in the research of landslide displacement dynamic forecasting. The Support Vector Machine (SVM) regression, we proposed, has been applied in Baishuihe landslide in Three Gorges Reservoir Area, China. The Oracle Data Mining (ODM) PL / SQL API have been introduced to build the SVM regression model for data mining process. The data was divided into two parts, wherein the first 36 months data used for training, and the other 6 months data used for validation. The results show that the error rate of the previous 5 was controlled within 8% and the accuracy of the 6th is 84.1%, which indicates SVM regression is reliable to calculate the displacement factors and can be used in short term prediction of landslide monitoring data.

scholarly journals A Landslide Displacement Prediction Method with Iteration-Based Combined Strategy

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
L. Li ◽  
S. X. Zhang ◽  
Y. Qiang ◽  
Z. Zheng ◽  
S. H. Li ◽  
...  
Keyword(s):  
Engineering Application ◽  
Prediction Method ◽  
Three Gorges Reservoir Area ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Displacement Prediction ◽  
The Three Gorges ◽  
Landslide Displacement Prediction ◽  
Grey Models ◽  
Combined Strategy

Predicting landslide displacement is of great significance in geotechnical engineering. An iteration-based combined prediction method was proposed for predicting the landslide displacement in this paper. Firstly, the landslide displacement was predicted by 10 latest multivariable grey models, and then the final landslide displacement prediction value was obtained through an iteration-based combined strategy. Concurrently, the reliability of the quadratic programming-based combined prediction method (QPCPM) and the iteration-based combined prediction method (ICPM) was rigorously proved in this paper. In addition, the inapplicability conditions of the optimal weight-based combined prediction method (OWCPM) were pointed out. ICPM could be regarded as a simplified version of QPCPM. The Bazimen and Baishuihe landslides in the Three Gorges Reservoir area of China were used as numerical examples to elaborate the performance of ICPM. This paper also demonstrated the reliability of ICPM by considering the effects of rainfall and reservoir water level on landslide displacement. Overall, ICPM features in simple and easy calculation and has rosy engineering application prospects.

MONITORING OF OPERATIONAL LOGISTIC PROCESSES IN GENERAL CARGO WAREHOUSES USING PREDICTIVE ANALYSIS

2019 ◽  
Vol 13 (1) ◽  
pp. 27-36
Author(s):  
Andreas Neubert
Keyword(s):  
Data Mining ◽  
Human Error ◽  
Predictive Analysis ◽  
Data Mining Algorithm ◽  
It Infrastructure ◽  
Monitoring Procedure ◽  
Manual Recording ◽  
Mining Tool ◽  
General Cargo ◽  
Different Characteristics

Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

Sign in / Sign up

Export Citation Format

Share Document