Delimitation of groundwater zones under contamination risk using a bagged ensemble of optimized DRASTIC frameworks - Barzegar, Rahim; Asghari Moghaddam, Asghar; Adamowski, Jan; Nazemi, Amir Hossein (Environmental Science and Pollution Research, 2019)

Evidence for the occurrence of hydrogeochemical processes in the groundwater of Khoy plain, northwestern Iran, using ionic ratios and geochemical modeling - Barzegar, Rahim; Asghari Moghaddam, Asghar; Nazemi, Amir Hossein; Adamowski, Jan (Environmental Earth Sciences, 2018)

Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model - Barzegar, Rahim; Asghari Moghaddam, Asghar; Adamowski, Jan; Ozga-Zielinski, Bogdan (Stochastic Environmental Research and Risk Assessment, 2018)

A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices - Vadiati, Meysam; Nalley, Deasy; Adamowski, Jan; Nakhaei, Mohammad; Asghari-Moghaddam, Asghar (Journal of Water and Land Development, 2019)
Abstract Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference systems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydrological data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the present research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.

Modification of the DRASTIC Framework for Mapping Groundwater Vulnerability Zones - Barzegar, Rahim; Asghari Moghaddam, Asghar; Norallahi, Shahrokh; Inam, Azhar; Adamowski, Jan; Alizadeh, Mohammad Reza; Bou Nassar, Jessica (Groundwater, 2020)
Abstract The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO 3 ) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin‐Shahr plain, Iran. Twenty‐two groundwater samples were collected from wells in the study area, and their respective NO 3 concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO 3 concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon–GA–DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R 2 of 0.22, the Wilcoxon–GA–DRASTIC obtained a maximum R 2 value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R 2 value to the highest degree. , Article impact statement : The results showed that both linear and nonlinear methods are useful in modifying the ratings and weights of the DRASTIC method for assessing the groundwater vulnerability.

Exploring the hydrogeochemical evolution of cold and thermal waters in the Sarein-Nir area, Iran using stable isotopes (δ18O and δD), geothermometry and multivariate statistical approaches - Barzegar, Rahim; Asghari Moghaddam, Asghar; Tziritis, Evangelos; Adamowski, Jan; Bou Nassar, Jessica; Noori, Mohammad; Aalami, Mohammad Taghi; Kazemian, Naeimeh (Geothermics, 2020)