… networks (ANN), since ANN is capable of non-linear modeling [4]. On the other hand, we
have attempted to apply memory-based learning (MBL) to daily water demand … water demand …

… with irrigation water demands in water distribution networks which are operated on … a
hybrid model based on memory to forecast the daily irrigation water demand at middle term time …

… water demand … daily water demand with multiple variables, called the attention-based
CNN-LSTM model, which combines convolutional neural network (CNN), long short-term memory (…

… This paper proposed the use of the long short-term memory (LSTM) method for short-term
urban water demand predictions, motivated by the fact that the LSTM-based model has …

… factors of water demand are selected by PCA method. In addition, two LSTM networks are
built to yield the daily urban water demand predictions using … Long short-term memory network …

… model based on long short-term memory (LSTM) neural networks to forecast hourly water …
In the world of water management, the constant increase of water demands from agriculture, …

… urban water demand forecasting (Hu et al. 2019; Kühnert et al. 2021). Accordingly, this
study applies an LSTM network to predict monthly water demand based on water consumption …

… of long-term and short-term memory networks (LSTM), bidirectional long-term memory …
water demand prediction, this paper used this method to predict short-term urban water demand …

… long short term memory network,’’ Appl. Energy, vol. 239, pp. … -term memory network,’’ Sci.
Total Environ., vol. 705, Feb. … -term memory networks for irrigation flow forecasting,’’ Agricult. …

… of an optimal plan for controlling water supply systems. Deep … Moreover, the water demand
forecasting model proposed in … amount of computations and memory to save the parameters …