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• W4385336257 endingPage "165884" @default.
• W4385336257 startingPage "165884" @default.
• W4385336257 abstract "Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource." @default.
• W4385336257 created "2023-07-29" @default.
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• W4385336257 date "2023-11-01" @default.
• W4385336257 modified "2023-10-14" @default.
• W4385336257 title "Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama" @default.
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• W4385336257 doi "https://doi.org/10.1016/j.scitotenv.2023.165884" @default.
• W4385336257 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37517717" @default.
• W4385336257 hasPublicationYear "2023" @default.
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