Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions
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- Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
- Chantal Donnelly & Wouter Greuell & Jafet Andersson & Dieter Gerten & Giovanna Pisacane & Philippe Roudier & Fulco Ludwig, 2017. "Erratum to: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level," Climatic Change, Springer, vol. 143(3), pages 535-535, August.
- Chantal Donnelly & Wouter Greuell & Jafet Andersson & Dieter Gerten & Giovanna Pisacane & Philippe Roudier & Fulco Ludwig, 2017. "Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level," Climatic Change, Springer, vol. 143(1), pages 13-26, July.
- Firat, Mahmut & Güngör, Mahmud, 2007. "River flow estimation using adaptive neuro fuzzy inference system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 75(3), pages 87-96.
- Salam A. Abbas & Yunqing Xuan, 2019. "Development of a New Quantile-Based Method for the Assessment of Regional Water Resources in a Highly-Regulated River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3187-3210, July.
- Fiaz Hussain & Ray-Shyan Wu & Jing-Xue Wang, 2021. "Comparative study of very short-term flood forecasting using physics-based numerical model and data-driven prediction model," 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. 107(1), pages 249-284, May.
- Renata Graf & Tomasz Kolerski & Senlin Zhu, 2022. "Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting," Resources, MDPI, vol. 11(2), pages 1-26, January.
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- Diego Copetti, 2023. "Integration of Water Quantity/Quality Needs with Socio-Economical Issues: A Focus on Monitoring and Modelling," Resources, MDPI, vol. 12(5), pages 1-4, May.
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Keywords
river flow; XGBoost algorithm; trends; multistep-ahead forecasting; climate variability; Ukraine;All these keywords.
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