Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Chen, Kunlong & Jiang, Jiuchun & Zheng, Fangdan & Chen, Kunjin, 2018. "A novel data-driven approach for residential electricity consumption prediction based on ensemble learning," Energy, Elsevier, vol. 150(C), pages 49-60.
- Yankun Wang & Huiming Tang & Tao Wen & Junwei Ma, 2020. "Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines," Complexity, Hindawi, vol. 2020, pages 1-15, May.
- Junwei Ma & Xiaoxu Niu & Huiming Tang & Yankun Wang & Tao Wen & Junrong Zhang, 2020. "Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach," Complexity, Hindawi, vol. 2020, pages 1-15, January.
- Xie Hu & Roland Bürgmann & William H. Schulz & Eric J. Fielding, 2020. "Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Zongxing Zou & Sha Lu & Fei Wang & Huiming Tang & Xinli Hu & Qinwen Tan & Yi Yuan, 2020. "Application of Well Drainage on Treating Seepage-Induced Reservoir Landslides," IJERPH, MDPI, vol. 17(17), pages 1-20, August.
- Emily Ying Yang Chan & Holly Ching Yu Lam, 2021. "Research in Health-Emergency and Disaster Risk Management and Its Potential Implications in the Post COVID-19 World," IJERPH, MDPI, vol. 18(5), pages 1-3, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
- Vahid Nourani & Nardin Jabbarian Paknezhad & Hitoshi Tanaka, 2021. "Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
- Hadjout, D. & Torres, J.F. & Troncoso, A. & Sebaa, A. & Martínez-Álvarez, F., 2022. "Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market," Energy, Elsevier, vol. 243(C).
- Fasheng Miao & Yiping Wu & Linwei Li & Kang Liao & Yang Xue, 2021. "Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(3), pages 2677-2696, February.
- Su-Ping Liu & Bin Shi & Kai Gu & Cheng-Cheng Zhang & Jian-Hui He & Jing-Hong Wu & Guang-Qing Wei, 2021. "Fiber-optic wireless sensor network using ultra-weak fiber Bragg gratings for vertical subsurface deformation monitoring," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(3), pages 2557-2573, December.
- Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
- Lixin Cheng & Qiuhua Tang & Zikai Zhang & Shiqian Wu, 2021. "Data mining for fast and accurate makespan estimation in machining workshops," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 483-500, February.
- Du, Xiaoyi & Wu, Dongdong & Yan, Yabo, 2023. "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China," Energy, Elsevier, vol. 262(PA).
- Kesriklioğlu, Esma & Oktay, Erkan & Karaaslan, Abdulkerim, 2023. "Predicting total household energy expenditures using ensemble learning methods," Energy, Elsevier, vol. 276(C).
- Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
- Li, Yanying & Che, Jinxing & Yang, Youlong, 2018. "Subsampled support vector regression ensemble for short term electric load forecasting," Energy, Elsevier, vol. 164(C), pages 160-170.
- Sun, Chuanwang & Xu, Mengjie & Wang, Bo, 2024. "Deep learning: Spatiotemporal impact of digital economy on energy productivity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
- Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
More about this item
Keywords
landslide displacement; predictive uncertainty; ensemble prediction; probability combination scheme; quantile regression neural networks (QRNNs); kernel density estimation (KDE);All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:13:p:4788-:d:379897. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.