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

Lion Algorithm- Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India