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

B S Supreetha,Narayan Shenoy,Prabhakar Nayak

doi:10.1155/2020/8685724

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.

Highlights

E hydrogeological Groundwater level (GWL) forecasting models are probabilistic, deterministic, and stochastic for the assessment of ground water (GW) systems. e traditional GW flow models are partial differential equations, which are embedded with simplifying assumptions about the aquifer properties and boundary conditions [3]. ese natural groundwater systems are complex and have a large number of parameters that are highly variable throughout time and space, such as aquifer parameters like hydraulic conductivity of the formation, groundwater storage, dimension of the aquifer, and other parameters related to the geological structure
It was observed that the hybrid long short-term memory-lion algorithm (LSTM-lion algorithm (LA)) model results are correlating better with the original data compared with the long short term memory (LSTM) and feedforward neural network (FFNN) and other approaches. us, the LSTM-LA approach predicts more accurately compared with the traditional feedforward approach
E current study has developed a new hybrid metaheuristic approach using the lion algorithm to optimise the weights of LSTM network for forecasting GWL. e precedent GWL and rainfall dataset from year 2000–2018 were accessed from government agencies. e observation well was located in a lateritic terrain in Udupi district, Karnataka, India. e results obtained from the propounded LSTM-LA model was compared with the basic FFNN and LSTM models. e FFNN model apprentice is in the randomised order, whereas feedback loops in LSTM enable to learn progressively. ere are several drawbacks exploiting standalone LSTM network

Summary

Research Article

Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India. The current research aimed at developing a groundwater level forecasting model by using hybrid long short-term memory-lion algorithm (LSTM-LA). Ey explored two machine learning algorithms LSTM and RNN to model and predict GW table response to storm events, using GW table, rainfall, and sea level as input parameters from 2010 to 2018 to train and test the model. E LSTM RNN has an internal state and may learn to forecast different series with good longterm memory, which is one of the most attractive and powerful features compared with traditional feedforward neural network (FFNN). E current study aims at developing a new hybrid metaheuristic approach using the LA to optimise the weights of LSTM network. E population of 2n lions are assigned to two groups as the candidate population. e best weights and biases are initialised with LA in the first epoch and are passed on to the LSTM network. e second step in the algorithm is the mating process that assures the lion’s survival as well as a platform for information exchange among different members. e new cubs are produced after selecting the female and male lions using linear combination of parents using mating operators as given in the following equations: Inputs

Ct ht Forgate gate ft

Yes criteria met?

Performance error

Conclusions