Assessment of groundwater salinity risk using kriging methods: A case study in northern Iran

Ashrafzadeh, Afshin; Roshandel, Fateme; Khaledian, Mohammadreza; Vazifedoust, Majid; Rezaei, Mojtaba

doi:10.1016/j.agwat.2016.09.028

Cited by 30 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sofiyan Abuelaish and Camacho Olmedo [71] developed a statistical model based on linear and multiple regression analysis to predict the 5-year ahead salinity value in the arid areas of Gaza, Palestine. Amiri-Bourkhani et al [72] and Ashrafzadeh et al [73] used geostatistical data and kriging methods to develop predictive models for probabilistic and zoning map of salinity in the arid areas of Iran. Time series analysis for modeling of groundwater salinity can be found in the literature [74], [75].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

et al. 2020

View full text Add to dashboard Cite

Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for groundwater quality monitoring and management. Advancement of accurate prediction systems is essential for the identification of vulnerable areas in order to raise awareness about the potential salinity susceptibility and protect the groundwater and top-soil in due time. In this study, three machine learning models of Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and Bayesian Generalized Linear Model (Bayesglm) are developed for building prediction models and their performance evaluated in the delineation of salinity susceptibility maps. Both natural and human effective factors (16 features) were used as predictors for groundwater salinity modeling and were randomly divided into the training (80%) and testing (20%) datasets. The models were evaluated using testing datasets after calibration using the selected features by recursive feature elimination (RFE) method. The RFE indicated that modeling with 8 features had better performance among 1 to 16 features (Accuracy = 0.87). Results of the groundwater salinity prediction highlighted that StoGB had a good performance, whereas the RotFor and Bayesglm had an excellent performance based on the Kappa values (> 0.85). Although spatial prediction of the models was different, all of the models indicated that central parts of the region have a very high susceptibility which matches with agricultural areas, lithology map, the locations with low depth to groundwater, low slope, and elevation. Additionally, areas near to the Maharlu lake and locations with a high decline in groundwater are also located in the very high susceptibility zone, which can confirm the effects of saltwater intrusion. The susceptibility maps produced in this study are of utmost importance for water security and sustainable agriculture.

show abstract

Section: Introductionmentioning

confidence: 99%

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It is possible that ammonia poisoning can occur at various salinity levels, as mentioned in the previous paragraphs [ 16 , 18 , 19 ]. Therefore, the need to investigate the interaction between ammonia and salinity on the physiology of freshwater fish is clear, On the other hand, so far there has been little discussion about this interference effect, Salinity and ammonia.…”

Section: Introductionmentioning

confidence: 99%

Combined effects of ammonia-N exposure and salinity changes on hematological and serum biochemical factors and thyroid hormones in Nile tilapia (Oreochromis niloticus)

Motamedi-Tehrani,

Peyghan,

Shahriari

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Groundwaters are important sources of drinking water in the world, so most of them are used without treatment. Thus, preservation of their quality is critical (Ashrafzadeh et al, 2016;Zheng & Ayotte, 2015).…”

Section: Introductionmentioning

confidence: 99%

Comparative health risk assessment of nitrate in drinking groundwater resources of urban and rural regions (Isfahan, Iran), using GIS

Aghapour

Bina

Tarrahi

et al. 2021

Environ Monit Assess

View full text Add to dashboard Cite

to NO 3 − exposure through consumption of drinking water was assessed, and the associated zoning maps were presented using geographic information system (GIS). Nitrate concentrations in the rural and urban areas were within 0.4-137 mg/L NO 3 − and 2.9-209 mg/L NO 3 − , respectively. Also, 226 (25%) and 104 (24%) of samples in the rural and urban areas, respectively, were detected above the Iran and WHO guideline NO 3 − values of 50 mg/L. The highest levels of NO 3 − , which were found in the western and central groundwater resources, occurred in the agricultural and residential areas. The NO 3 − concentrations were higher in urban than rural areas in the many studied counties. Also, nitrate was higher in wet seasons than in dry ones. Infants' non-carcinogenic risks were higher than the other groups. Infants (HQ > 1) were the most vulnerable group compared with the other groups in some counties. Thus, there are potential risks of methemoglobinemia, especially for infants. It is critical to adopt specific strategies to reduce the nitrate concentration in the studied groundwater.

show abstract

Assessment of groundwater salinity risk using kriging methods: A case study in northern Iran

Cited by 30 publications

References 21 publications

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

Combined effects of ammonia-N exposure and salinity changes on hematological and serum biochemical factors and thyroid hormones in Nile tilapia (Oreochromis niloticus)

Comparative health risk assessment of nitrate in drinking groundwater resources of urban and rural regions (Isfahan, Iran), using GIS

Contact Info

Product

Resources

About