Investigation of quantitative and qualitative changes in groundwater of Ardebil plain using ensemble artificial intelligence-based modeling

Abstract

Groundwater is an essential source to supply water for various sectors. This paper aimed to predict the quantitative and qualitative changes in groundwater over time and to evaluate the efficiency of different modeling methods. This study is based on three steps. In the first step, quantitative and qualitative piezometers were clustered by the Growing Neural Gas Network (GNG) method, and the central piezometer of each cluster was used on behalf of each cluster. In the second step, four different Artificial Intelligence (AI) models were applied, namely Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Emotional Artificial Neural Network (EANN). As a post-processing approach three different ensemble methods were used: simple average ensemble (SAE), weighted average ensemble (WAE), and nonlinear neural network ensemble (NNE). In the third step, the outputs of single AI models were used to enhance the evaluation results. Therefore, the results demonstrate that the NNE led to reach the better performance for three GWL, TDS, and TH parameters up to 37, 29, and 23% on average, respectively. Study results will lead to the improvement of AI applications in groundwater research and will benefit groundwater development plans. HIGHLIGHTS Artificial intelligence methods were used to predict quantitative and qualitative changes in groundwater.; The GNG method was used for clustering.; Ensemble artificial intelligence-based modeling was employed to enhance the individual modeling results.; The results show better performance when using ensemble artificial intelligence-based modeling.;

Keywords

• black-box
• clustering
• groundwater parameters
• model combination