  Groundwater is a precious natural resource. Groundwater level (GWL)
forecasting is crucial in the field of water resource management. Measurement
of GWL from observation-wells is the principle source of information about the
aquifer and is critical to its evaluation. Most part of the Udupi district of
Karnataka State in India consists of geological formations: lateritic terrain
and gneissic complex. Due to the topographical ruggedness and inconsistency in
rainfall, the GWL in Udupi region is declining continually and most of the open
wells are drying-up during the summer. Hence, the current research aimed at
developing a groundwater level forecasting model by using hybrid Long
Short-term Memory-Lion Algorithm (LSTM-LA). The historical GWL and rainfall
data from an observation well from Udupi district, located in Karnataka state,
India, were used to develop the model. The prediction accuracy of the hybrid
LSTM-LA model was better than that of the Feedforward Neural network (FFNN) and
the isolated LSTM models. The hybrid LSTM-LA based forecasting model is
promising for a larger dataset.
