Temporal and spatial estimation of groundwater electrical conductivity using soft computing approaches: Analysis of East Azerbaijan Province, Iran

Abstract

The evaluation of groundwater quality plays an important role in the management of groundwater. The main objectives of the present work are to develop a novel soft computing framework including Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet-GEP (WGEP) for the temporal and spatial estimation of groundwater electrical conductivity (EC) in the East Azerbaijan province, Iran over 2001–2020. The results demonstrate the importance of wavelet transform application; the performance percentage enhancement of the WANFIS and WGEP models compared to the ANFIS and GEP, using the RMSE criterion, ranged from 15.48 to 51.09% and from 5.06 to 86.95%, respectively. All the developed models showed the WGEP superior compared to others. The impact of land use characteristics, climatic conditions, and geological features on groundwater quality showed that there is a direct relationship between the extent of agricultural land, semi-arid climate conditions and groundwater EC amounts. The results demonstrated that the values of EC increase from east to west, indicating the direct exchange of surface and groundwater in the study area. Moreover, groundwater quality changes significantly across the width of the fault, with groundwater EC in the northern part of the fault higher than that in the southern part. HIGHLIGHTS The EC variable of groundwater resources was estimated via single and hybrid-wavelet soft computing approaches.; The impact of land use characteristics, climatic conditions, and geological features on groundwater quality was investigated.; The data de-noising by wavelet approaches have the ability to improve the performance of EC estimation at spatial–temporal scales.;

Keywords

• climate
• geological structure
• groundwater resources
• land use feature
• wavelet theory