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    Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model 

    Source: Journal of Water Resources Planning and Management:;2020:;Volume ( 146 ):;issue: 009
    Author(s): Li Mu; Feifei Zheng; Ruoling Tao; Qingzhou Zhang; Zoran Kapelan
    Publisher: ASCE
    Abstract: This case study uses a long short-term memory (LSTM)–based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated ...
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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