A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning
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DOI: 10.1007/s11269-022-03255-5
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- Salih Muhammad Awadh & Heba Al-Mimar & Zaher Mundher Yaseen, 2021. "Groundwater availability and water demand sustainability over the upper mega aquifers of Arabian Peninsula and west region of Iraq," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 1-21, January.
- Mukand Babel & Nisuchcha Maporn & Victor Shinde, 2014. "Incorporating Future Climatic and Socioeconomic Variables in Water Demand Forecasting: A Case Study in Bangkok," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2049-2062, May.
- Shan Zou & Abuduwaili Jilili & Weili Duan & Philippe De Maeyer & Tim Van de Voorde, 2019. "Human and Natural Impacts on the Water Resources in the Syr Darya River Basin, Central Asia," Sustainability, MDPI, vol. 11(11), pages 1-18, May.
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- Ke Wang & Zanting Ye & Zhangquan Wang & Banteng Liu & Tianheng Feng, 2023. "MACLA-LSTM: A Novel Approach for Forecasting Water Demand," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
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Keywords
Multifarious factors; Time series; Base learner; Local extreme values; Volatility;All these keywords.
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