Optimised stacking generalisation methodology for groundwater level prediction

Abstract Global climate change has brought a paradigm shift in groundwater management practices. For a sustainable groundwater supply, there is a need to predict the availability of groundwater due to the intermittent fluctuations in the discharge and recharge phenomena. This study proposes an optimised stacking generalisation methodology for groundwater level prediction. The proposed methodology had the Particle Swarm Optimization-Artificial Neural Network (PSO-ANN), Genetic Algorithm-Artificial Neural Network (GA-ANN) and Self-Adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) as the base learners. Intercomparison revealed the prediction strength of SaDE-ELM over the other base learners. Hence, the stacked model was formed when the SaDE-ELM was used as the meta learner. Statistical analyses show that the proposed stacking method performed better than the standalone hybrid PSO-ANN, GA-ANN and SaDE-ELM with average RMSE and R values of 0.2209 m and 0.78, respectively. The superiority of the stacking method was further revealed using the Taylor diagram to present the statistical comparison with the observations of all models used.