article · Water
Predicting daily streamflow in semi-arid regions is critical for water resource management yet remains difficult due to complex hydrological conditions. A study evaluated Long Short-Term Memory deep learning networks to simulate daily streamflow in the Ait Ouchene watershed within Morocco's Oum Er-Rbia river basin. The model was trained and tested using a combination of in situ and remotely sensed hydroclimatic data collected between 2001 and 2010. Researchers assessed different data splitting techniques, sequence lengths, and input feature scenarios. Using default inputs, the model achieved modest accuracy, reaching a coefficient of determination of 0.58 over a 30-day sequence. However, combining lagged data inputs with Forward Feature Selection significantly improved performance, achieving an accuracy of 0.84 over a 20-day sequence. The findings show that deep learning paired with targeted feature selection can deliver reliable streamflow forecasts even where data are limited.
Managing water resources in dry and semi-arid environments depends on accurate streamflow forecasts, which are often hindered by limited monitoring data. Demonstrating that machine learning models can accurately predict river flows using satellite and local records allows water authorities to anticipate water availability, improve catchment management, and make informed operational decisions even in data-scarce conditions.
This work demonstrates an applied and tested analytical approach suitable for integration into hydrological forecasting systems and water resource planning tools. Potential end users include river basin agencies, environmental consultancies, and public water authorities managing arid or semi-arid catchments. The methodology is currently at an applied research stage, validated on a single watershed, and would require software engineering and operational data pipelines before reaching commercial deployment.
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Daily hydrological modelling is among the most challenging tasks in water resource management, particularly in terms of streamflow prediction in semi-arid areas. Various methods were applied in order to deal with this complex phenomenon, but recently data-driven models have taken a better space, given their ability to solve prediction problems in time series. In this study, we have employed the Long Short-Term Memory (LSTM) network to simulate the daily streamflow over the Ait Ouchene watershed (AIO) in the Oum Er-Rbia river basin in Morocco, based on a temporal sequence of in situ and remotely sensed hydroclimatic data ranging from 2001 to 2010. The analysis adopted in this work is based on three-dimension input required by the LSTM model (1); the input samples used three splitting approaches: 70% of the dataset as training, splitting the data considering the hydrological year and the cross-validation method; (2) the sequence length; (3) and the input features using two different scenarios. The prediction results demonstrate that the LSTM performs poorly using the default data input scenario, whereas the best results during the testing were found in a sequence length of 30 days using approach 3 (R2 = 0.58). In addition, the LSTM fed with the lagged data input scenario using the Forward Feature Selection (FFS) method provides high performance accuracy using approach 2 (R2 = 0.84) in a sequence length of 20 days. Eventually, in applications related to water resources management where data are limited, the use of the deep learning technique is able to create high predictive accuracy, which can be enhanced with the right combination subset of features by using FFS.
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DOI: 10.3390/w15020262
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