Relationship between groundwater quality and distance to fault using adaptive neuro fuzzy inference system (ANFIS) and geostatistical methods (case study: North of Fars Province)

• Published in: Spatial information research (Online) Vol. 27; no. 5; pp. 529 - 538
• Main Authors: Aghajari, Maryam, Mozayyan, Maleeha, Mokarram, Marzieh, Chekan, Alireza Amirian
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.10.2019; 대한공간정보학회
• Subjects: Database Management; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Remote Sensing/Photogrammetry; 공학일반; Groundwater quality prediction; Ground statistic methods; Fault; ANFIS model
• ISSN: 2366-3286; 2366-3294
• DOI: 10.1007/s41324-019-00253-5

The aim of this paper is to use Kriging (spherical, exponential, and Guassian models) and Inverse distance weighted (IDW) methods to prepare the water quality map. In addition, the relationship between water quality and distance to fault is determined in northeast of Fars province, Iran. Adaptive neuro fuzzy inference system method is also used to predict groundwater quality. The measured Sodium adsorption ratio and electrical conductivity parameters that are obtained from 384 wells in 2005 to 2014 are utilized to determine groundwater quality. The results show that the Kriging method (spherical model) has a higher accuracy with lower RMSE value than IDW method. Thereafter, this model is used to prepare the interpolation maps. Moreover, the results indicate the hybrid model in terms of maximum R 2 and the minimum error is suitable enough to predict water quality parameters. In addition, the results depict by increasing the number of fault, the groundwater quality is decreased and vice versa.