Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach
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- Ernst Wit & Edwin van den Heuvel & Jan-Willem Romeijn, 2012. "‘All models are wrong...’: an introduction to model uncertainty," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 217-236, August.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Tao Guo & Wei He & Zhonglian Jiang & Xiumin Chu & Reza Malekian & Zhixiong Li, 2018. "An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level," Energies, MDPI, vol. 12(1), pages 1-11, December.
- Thomas J. Mack & Michael P. Chornack & Mohammad R. Taher, 2013. "Groundwater-level trends and implications for sustainable water use in the Kabul Basin, Afghanistan," Environment Systems and Decisions, Springer, vol. 33(3), pages 457-467, September.
- Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
- Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
- Dimitrios Myronidis & Konstantinos Ioannou & Dimitrios Fotakis & Gerald Dörflinger, 2018. "Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1759-1776, March.
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Keywords
ARIMA; Kabul River; machine learning; floods; droughts; forecasting;All these keywords.
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