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

TL;DR

This paper introduces a hybrid LSTM-Lion Algorithm model for groundwater level forecasting in Udupi, India, demonstrating improved accuracy over traditional neural network models for water resource management.

Contribution

The study develops a novel hybrid LSTM-Lion Algorithm model specifically for groundwater level prediction, outperforming existing neural network approaches.

Findings

Hybrid LSTM-LA model shows higher accuracy than FFNN and standalone LSTM.

Model effectively predicts groundwater levels in complex terrains.

Promising results for larger datasets in water resource management.

Abstract

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.