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Related Experiment Video

Updated: Oct 21, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Reliability evaluation of groundwater quality index using data-driven models.

Mohammad Najafzadeh¹, Farshad Homaei², Sedigheh Mohamadi³

¹Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran. moha.najafzadeh@gmail.com.

Environmental Science and Pollution Research International

|September 5, 2021

Summary

Groundwater quality in Iran

Keywords:

Artificial intelligence models Drinking water suitability Groundwater resources Monte-Carlo analysis Probabilistic water quality modeling

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Area of Science:

Environmental science
Hydrology
Water resource management

Background:

Groundwater quality assessment is crucial for resource management, especially in arid regions.
The Rafsanjan Plain in Iran faces challenges in managing groundwater resources.

Purpose of the Study:

To evaluate groundwater quality index (GQI) for various uses in the Rafsanjan Plain.
To predict GQI using Data-Driven Techniques (DDTs) and assess uncertainties.

Main Methods:

Applied four DDTs: Evolutionary Polynomial Regression (EPR), Model Tree (MT), Gene-Expression Programming (GEP), and Multivariate Adaptive Regression Spline (MARS).
Utilized 1349 monthly observations of 12 groundwater quality parameters.
Employed a reliability-based probabilistic model with Monte-Carlo simulation for uncertainty analysis.

Main Results:

EPR showed the highest accuracy in predicting GQI compared to MT, GEP, and MARS.
The study found a high probability (nearly 100%) of "Excellent" groundwater quality for the region.

Conclusions:

Accurate GQI prediction using DDTs, particularly EPR, enhances groundwater management.
Probabilistic assessment indicates favorable groundwater quality in the Rafsanjan Plain, with a slight decrease in the probability of "Good" quality.