Study of the spatial distribution of groundwater quality using soft computing and geostatistical models

Abstract

Groundwater quality monitoring and simulation is of great importance in arid and semi-arid regions due to water scarcity in such regions. Geostatistical methods are appropriate techniques for interpolating and spatial mapping of groundwater qualitative parameters. In the present study, geostatistic-based Kriging and Co-Kriging methods were used and compared with data-driven artificial neural network (ANN) and ANFIS models for predicting spatial distribution of groundwater electrical conductivity (EC), then the best model was selected for further sampling in the studied region. Data from 24 wells in the Keshit, Bam Normashir, and Rhmtabad plains (Kerman province, Iran) during the period of 2002–2011 were used for analysis. Root mean square error (RMSE), R ² , and mean absolute error (MAE) statistical indices were used for assessing the applied models. Results showed that for both the models, i.e. triple input model (including longitude, latitude, and the number of months) and the quadruple-input model (including longitude, latitude, number of months, and Cl), ANN model presented the most accurate results with the lowest RMSE and MAE and the highest R ² values. Attending to the geostatistical methods, Co-Kriging method outperformed the Kriging method. Finally, using the tested geostatistical model, the EC data were produced for the regions without observational values.

Keywords:

• Adaptive neuro-fuzzy inference system
• artificial neural network
• geostatistics
• geographical information