Groundwater level Simulation and forecasting using ANN at Wadi –Nyala watershed, Darfur Sudan

• 1、College of Hydrology and Water Resources, Hohai university

Abstract：A proper design of the architecture of Artificial Neural Network (ANN) models can provide a robust tool in water resources modeling and forecasting. The performance of neural networks in a groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the decreasing trend of the groundwater level and provide acceptable predictions up to 17 months ahead. Wadi –Nyala watershed in the south Darfur Nyala, Sudan. Was chosen as the study area as its groundwater resources have being overexploited during the last twenty years and the groundwater level has been decreasing steadily. The model efficiency and accuracy were measured based on the root mean square error (RMSE) and regression coefficient ( ). The model provided the best fit and the predicted trend followed the observed data closely (RMSE = 0.445 and = 0.973). Thus, for precise and accurate groundwater level simulation, ANN appears to be a promising tool.

keywords： Artificial neural networks back-propagation Feed-forward simulation groundwater level

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