Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data

Abstract

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), and the jellyfish search optimizer (JFO) in groundwater level estimation. Daily precipitation and temperature datasets acquired from two stations in northern Bangladesh were used as inputs to the models, which were evaluated based on different quantitative statistics and assessed based on RMSE, MAE, R2, and some new graphical inspection methods. The outcomes of the applications revealed that the efficiency of ELM models was considerably improved by using metaheuristic algorithms. The ELM-JSO improved the RMSE of the standalone ELM model by 13% for the optimal precipitation, temperature, and groundwater level inputs in the testing stage. Among the implemented methods, the ELM-JFO performed the best in estimating the daily groundwater level, and the ELM-WOA and ELM-HHO, respectively, followed it. Viability of a new extreme machine learning (ELM) method tuned with Jellyfish search optimizer (JFO) is investigated in groundwater level estimation. The ELM-JFO is compared with hybrid ELM-PSO, ELM-WOA and ELM-HHO models using daily precipitation and temperature data acquired from two stations of Bangladesh. The ELM-JSO improves the root mean square error of the standalone ELM model by 13% for the optimal precipitation, temperature and groundwater level inputs.