Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
- A. N. Pettitt, 1979. "A Non‐Parametric Approach to the Change‐Point Problem," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(2), pages 126-135, June.
- Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
- Erdem, Orhan & Ceyhan, Elvan & Varli, Yusuf, 2014.
"A new correlation coefficient for bivariate time-series data,"
Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 274-284.
- Orhan Erdem & Elvan Ceyhan & Yusuf Varlı, 2011. "A New Correlation Coefficient for Bivariate Time-Series Data," Working Papers 201101, Murat Sertel Center for Advanced Economic Studies, Istanbul Bilgi University.
- A. I. McLeod & W. K. Li, 1983. "Diagnostic Checking Arma Time Series Models Using Squared‐Residual Autocorrelations," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 269-273, July.
- Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
- Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
- Fangqin Zhang & Yan Kang & Xiao Cheng & Peiru Chen & Songbai Song, 2022. "A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3673-3697, August.
- Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
- Wenxin Xu & Jie Chen & Xunchang J. Zhang, 2022. "Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3609-3625, August.
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.- Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.
- Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
- Alagidede, Paul & Panagiotidis, Theodore, 2009.
"Modelling stock returns in Africa's emerging equity markets,"
International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
- Paul Alagidede & Theodore Panagiotidis, 2009. "Modelling stock returns in Africa’s emerging equity markets," Discussion Paper Series 2009_01, Department of Economics, University of Macedonia, revised Jan 2009.
- Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," Stirling Economics Discussion Papers 2009-04, University of Stirling, Division of Economics.
- Chen, Shyh-Wei & Xie, Zixiong, 2015. "Testing for current account sustainability under assumptions of smooth break and nonlinearity," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 142-156.
- Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
- de Lima, Pedro J. F., 1997. "On the robustness of nonlinearity tests to moment condition failure," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 251-280.
- Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005.
"Mean and variance causality between the Cyprus Stock Exchange and major equity markets,"
Working Papers
0501, University of Crete, Department of Economics.
- Georgios Kouretas & Eleni Constantinou & Robert Georgiades & Avo Kazandjian, 2005. "Mean and variance causality between the Cyprus Stock Exchange and major equity markets," Money Macro and Finance (MMF) Research Group Conference 2005 24, Money Macro and Finance Research Group.
- Baur, Dirk & Jung, Robert C., 2006. "Return and volatility linkages between the US and the German stock market," Journal of International Money and Finance, Elsevier, vol. 25(4), pages 598-613, June.
- Theodore Panagiotidis, 2010.
"Market efficiency and the Euro: the case of the Athens stock exchange,"
Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(3), pages 237-251, July.
- Theodore Panagiotidis, 2003. "Market Efficiency and the Euro:The case of the Athens Stock Exchange," Public Policy Discussion Papers 03-08, Economics and Finance Section, School of Social Sciences, Brunel University.
- Theodore Panagiotidis, 2005. "Market Efficiency and the Euro: The case of the Athens Stock Exchange," Finance 0507022, University Library of Munich, Germany.
- Theodore Panagiotidis, 2003. "Market Efficiency and the Euro:The case of the Athens Stock Exchange," Economics and Finance Discussion Papers 03-08, Economics and Finance Section, School of Social Sciences, Brunel University.
- Theodore Panagiotidis, 2008. "Market Efficiency and the Euro: The case of the Athens Stock exchange," Discussion Paper Series 2008_14, Department of Economics, University of Macedonia, revised Dec 2008.
- Zacharias Psaradakis & Marián Vávra, 2019.
"Portmanteau tests for linearity of stationary time series,"
Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 248-262, February.
- Marian Vavra, 2012. "Testing Non-linearity Using a Modified Q Test," Birkbeck Working Papers in Economics and Finance 1204, Birkbeck, Department of Economics, Mathematics & Statistics.
- Zacharias Psaradakis & Marian Vavra, 2016. "Portmanteau Tests for Linearity of Stationary Time Series," Working and Discussion Papers WP 1/2016, Research Department, National Bank of Slovakia.
- Zacharias Psaradakis & Marián Vávra, 2015. "Portmanteau Tests for Linearity of Stationary Time Series," Birkbeck Working Papers in Economics and Finance 1514, Birkbeck, Department of Economics, Mathematics & Statistics.
- Tiwari, Aviral Kumar & Aye, Goodness C. & Gupta, Rangan & Gkillas, Konstantinos, 2020.
"Gold-oil dependence dynamics and the role of geopolitical risks: Evidence from a Markov-switching time-varying copula model,"
Energy Economics, Elsevier, vol. 88(C).
- Aviral Kumar Tiwari & Goodness C. Aye & Rangan Gupta & Konstantinos Gkillas, 2019. "Gold-Oil Dependence Dynamics and the Role of Geopolitical Risks: Evidence from a Markov-Switching Time-Varying Copula Model," Working Papers 201918, University of Pretoria, Department of Economics.
- Theodore Panagiotidis, 2002. "Testing the assumption of Linearity," Economics Bulletin, AccessEcon, vol. 3(29), pages 1-9.
- Sina Montazeri & Akram Mirzaeinia & Haseebullah Jumakhan & Amir Mirzaeinia, 2024. "CNN-DRL for Scalable Actions in Finance," Papers 2401.06179, arXiv.org.
- David Vidal-Tomás & Simone Alfarano, 2020.
"An agent-based early warning indicator for financial market instability,"
Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 49-87, January.
- David Vidal-Tomás & Simone Alfarano, 2018. "An agent based early warning indicator for financial market instability," Working Papers 2018/12, Economics Department, Universitat Jaume I, Castellón (Spain).
- Vidal-Tomás, David & Alfarano, Simone, 2018. "An agent based early warning indicator for financial market instability," MPRA Paper 89693, University Library of Munich, Germany.
- Bock, David & Pettersson, Kjell, 2007. "Explorative analysis of spatial aspects on the Swedish influenza data," Research Reports 2007:10, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
- Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
- Kazi Ali Tamaddun & Ajay Kalra & Sajjad Ahmad, 2019. "Spatiotemporal Variation in the Continental US Streamflow in Association with Large-Scale Climate Signals Across Multiple Spectral Bands," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1947-1968, April.
- Noura Metawa & Mohamemd I. Alghamdi & Ibrahim M. El-Hasnony & Mohamed Elhoseny, 2021. "Return Rate Prediction in Blockchain Financial Products Using Deep Learning," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
- Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
- Alina Bărbulescu & Cristian Ștefan Dumitriu, 2021. "On the Connection between the GEP Performances and the Time Series Properties," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
More about this item
Keywords
monthly runoff prediction; machine learning; copula entropy; stepwise regression; Upper Yangtze River;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:jsusta:v:14:y:2022:i:18:p:11149-:d:908023. 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.