ArticleScientific reports2026
Forecasting of global water usage in agriculture and total global consumption by using the Bi-GRU model.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Water resources should be used efficiently in order to reduce constraints to future generations. it is important to stress the importance of well-developed management plans with sound forecasts. The need to have exact long-term forecast, limited access to data, changeable demand and some of the significant challenges it faces include integration of different datasets. The objective of this research is to predict Total Water consumption (TWC) and agricultural water use (AWU) which are increasing because of causes like climate change, seasonal highs and lows and financial requirements. Forecasting It is difficult to plan what to do with watering because of the unpredictable weather and soil conditions. In this Research, a Bidirectional Gated Recurrent Unit (Bi-GRU) is applied on the Global Water. These challenges to strategy could be addressed by the Consumption Dataset (2000–2024) in order to obtain the most accurate prediction. This is further enhanced by the fact that the performance of our model (Bi-GRU) is compared to that of other popular models like LSTM, Deep-AR, Mega-CRN and TFT. The play is very active. with an effective mean absolute percentage error (MAPE) of 0.2257, root mean square error (RMSE) by less than the fact that the mean squared error (MSE) = 0.0049 and the mean absolute error (MAE) = 0.0587 confirm that the Bi-GRU model is superior compared to other models used to forecast TWC and AWU that are also our dependent variables. The higher precision in forecasting rendered feasible by using deep learning algorithms enables a more precise evaluation of global water consumption particularly that of the agricultural and industrial sectors. It thus facilitates the development of efficient water-management schemes globally. The choice of Bi-GRU is motivated by its bidirectional temporal learning capability, which enables improved modeling of long-term and multivariate dependencies in global water consumption time-series.
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