Publications by authors named "Nima Chitsazan"

3 Publications

  • Page 1 of 1

Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran.

J Environ Health Sci Eng 2016 9;14:13. Epub 2016 Aug 9.

Research Engineer at EnTech Engineering, PC11 broadway 21st floor, New York, NY 10004 USA.

Background: Extensive human activities and unplanned land uses have put groundwater resources of Shiraz plain at a high risk of nitrate pollution, causing several environmental and human health issues. To address these issues, water resources managers utilize groundwater vulnerability assessment and determination of protection. This study aimed to prepare the vulnerability maps of Shiraz aquifer by using Composite DRASTIC index, Nitrate Vulnerability index, and artificial neural network and also to compare their efficiency.

Methods: The parameters of the indexes that were employed in this study are: depth to water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, hydraulic conductivity, and land use. These parameters were rated, weighted, and integrated using GIS, and then, used to develop the risk maps of Shiraz aquifer.

Results: The results indicated that the southeastern part of the aquifer was at the highest potential risk. Given the distribution of groundwater nitrate concentrations from the wells in the underlying aquifer, the artificial neural network model offered greater accuracy compared to the other two indexes. The study concluded that the artificial neural network model is an effective model to improve the DRASTIC index and provides a confident estimate of the pollution risk.

Conclusions: As intensive agricultural activities are the dominant land use and water table is shallow in the vulnerable zones, optimized irrigation techniques and a lower rate of fertilizers are suggested. The findings of our study could be used as a scientific basis in future for sustainable groundwater management in Shiraz plain.
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http://dx.doi.org/10.1186/s40201-016-0254-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977699PMC
August 2016

Bayesian Chance-Constrained Hydraulic Barrier Design under Geological Structure Uncertainty.

Ground Water 2015 Nov-Dec;53(6):908-19. Epub 2014 Dec 15.

Department of Civil and Environmental Engineering, Louisiana State University, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803.

The groundwater community has widely recognized geological structure uncertainty as a major source of model structure uncertainty. Previous studies in aquifer remediation design, however, rarely discuss the impact of geological structure uncertainty. This study combines chance-constrained (CC) programming with Bayesian model averaging (BMA) as a BMA-CC framework to assess the impact of geological structure uncertainty in remediation design. To pursue this goal, the BMA-CC method is compared with traditional CC programming that only considers model parameter uncertainty. The BMA-CC method is employed to design a hydraulic barrier to protect public supply wells of the Government St. pump station from salt water intrusion in the "1500-foot" sand and the "1700-foot" sand of the Baton Rouge area, southeastern Louisiana. To address geological structure uncertainty, three groundwater models based on three different hydrostratigraphic architectures are developed. The results show that using traditional CC programming overestimates design reliability. The results also show that at least five additional connector wells are needed to achieve more than 90% design reliability level. The total amount of injected water from the connector wells is higher than the total pumpage of the protected public supply wells. While reducing the injection rate can be achieved by reducing the reliability level, the study finds that the hydraulic barrier design to protect the Government St. pump station may not be economically attractive.
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http://dx.doi.org/10.1111/gwat.12304DOI Listing
July 2016

A hierarchical Bayesian model averaging framework for groundwater prediction under uncertainty.

Ground Water 2015 Mar-Apr;53(2):305-16. Epub 2014 May 28.

Department of Civil and Environmental Engineering, Louisiana State University, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803.

Groundwater prediction models are subjected to various sources of uncertainty. This study introduces a hierarchical Bayesian model averaging (HBMA) method to segregate and prioritize sources of uncertainty in a hierarchical structure and conduct BMA for concentration prediction. A BMA tree of models is developed to understand the impact of individual sources of uncertainty and uncertainty propagation to model predictions. HBMA evaluates the relative importance of different modeling propositions at each level in the BMA tree of model weights. The HBMA method is applied to chloride concentration prediction for the "1,500-foot" sand of the Baton Rouge area, Louisiana from 2005 to 2029. The groundwater head data from 1990 to 2004 is used for model calibration. Four sources of uncertainty are considered and resulted in 180 flow and transport models for concentration prediction. The results show that prediction variances of concentration from uncertain model elements are much higher than the prediction variance from uncertain model parameters. The HBMA method is able to quantify the contributions of individual sources of uncertainty to the total uncertainty.
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http://dx.doi.org/10.1111/gwat.12207DOI Listing
January 2016
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