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

Content Provider	Springer Nature Link
Author	Huang, Faming Yin, Kunlong Zhang, Guirong Gui, Lei Yang, Beibei Liu, Lei
Copyright Year	2016
Abstract	Landslide displacement system is generally characterized by non-stationary and nonlinear characteristics. Traditionally, many artificial neural network (ANN) models have been proposed to forecast landslide displacement. However, the underlying non-stationary characteristics in the landslide displacement are not captured, and the input–output variables of the ANN models are not selected nonlinearly. To overcome these drawbacks, this paper proposes the chaos theory-based discrete wavelet transform (DWT)–extreme learning machine (ELM) model to predict landslide displacement. The DWT method is adopted to decompose the landslide displacement into several low- and high-frequency components to address the non-stationary characteristics. And chaos theory is used to determine the input–output variables of the ELM model. The cumulative displacement time series of the Baishuihe and Baijiabao landslides in the Three Gorges Reservoir Area, China, are used as data sets. The results show that the chaotic DWT-ELM model accurately predicts landslide displacement. The chaotic DWT–support vector machine (SVM), chaotic DWT–back-propagation neural network (BPNN) and single chaotic ELM models are used for comparisons. The comparison results show that the chaotic DWT-ELM model achieves higher prediction accuracy than do the chaotic DWT-SVM, chaotic DWT-BPNN and the single chaotic ELM models.
Starting Page	1
Ending Page	18
Page Count	18
File Format	PDF
ISSN	18666280
Journal	Environmental Earth Sciences
Volume Number	75
Issue Number	20
e-ISSN	18666299
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-10-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Reservoir landslide Displacement prediction Chaos theory Phase space reconstruction Discrete wavelet transform Extreme learning machine Geology Hydrology/Water Resources Geochemistry Environmental Science and Engineering Terrestrial Pollution Biogeosciences
Content Type	Text
Resource Type	Article
Subject	Global and Planetary Change Earth-Surface Processes Soil Science Environmental Chemistry Pollution Geology Water Science and Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Unload–load displacement response ratio parameter and its application in prediction of debris landslide induced by rainfall

A hybrid computational intelligence method for predicting dew point temperature

Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide

Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China

Potential for a Ganhaizi landslide-generated surge in Xiluodu Reservoir, Jinsha River, China

Effect of hydraulic properties of soil and fluctuation velocity of reservoir water on landslide stability

Early landslide detection based on D-InSAR technique at the Wudongde hydropower reservoir

Modeling of landslide generated impulsive waves considering complex topography in reservoir area

Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets

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

Similar Documents

Unload–load displacement response ratio parameter and its application in prediction of debris landslide induced by rainfall

A hybrid computational intelligence method for predicting dew point temperature

Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide

Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China

Potential for a Ganhaizi landslide-generated surge in Xiluodu Reservoir, Jinsha River, China

Effect of hydraulic properties of soil and fluctuation velocity of reservoir water on landslide stability

Early landslide detection based on D-InSAR technique at the Wudongde hydropower reservoir

Modeling of landslide generated impulsive waves considering complex topography in reservoir area

Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets

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