Optimised Stacking Generalisation Methodology for Groundwater Level Prediction

In this current study, the stacking method, which is based on optimised artificial intelligence (AI), was employed. The methods used are 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). In the first level classifier, rainfall, temperature and evaporation were used as the input parameters to predict groundwater level (GWL), which was the output parameter. The best predictor among the PSO-ANN, GA-ANN and SaDE-ELM was selected and used as the meta-learner. At this stage, the predicted GWL data from the optimal base-learners was used as input. Statistical analyses show that the proposed stacking method performed better than the standalone hybrid AI models, 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.