Comparision of Birjand Plain Aquifer Chromium Monitoring Network Using Principal Component Analysis (PCA) and Entropy Theory

Khashei, Abbas; Shahidi, Ali; Rahnama, Samira

doi:10.22034/jewe.2020.254396.1448

Document Type : Research Paper

Authors

¹ Assoc. Professor, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran

² 2Ph.D. Scholar, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran

https://doi.org/10.22034/jewe.2020.254396.1448

Abstract

In order to efficiently and effectively manage groundwater resources, it is very important to determine the important points for sampling in terms of reducing the sample size and saving cost and time. In this study, the main chrome monitoring network of Birjand plain aquifer was designed using principal component analysis method and entropy theory, which is a practical method in evaluating quality monitoring systems. For this purpose, 25 aquifers of Birjand plain with a statistical length of 5 yr (2015-2019) were surveyed. In the study area, the average annual chromium (hexavalent) of groundwater was studied using principal component analysis technique and entropy theory to determine the effective sampling wells in the aquifer of this plain. The results showed that out of 25 wells in the study area, 15 wells can be introduced as groundwater chromium index wells of Birjand plain aquifer having a good distribution in the area that can play an important role in reducing sampling costs. Moreover, in order to consider the time factor in the changes, this method was performed in two time periods of 2 and 3 yr. The results showed that in the period of 2 yr (2015-2016), 19 wells were selected as effective wells, while the number reduced to 17 wells in the period of 3 yr (2017-2019). Entropy theory showed that all wells in the region are of equal importance in monitoring network design.

Keywords

Main Subjects

Water Pollution

References

Alves, J. P. H. A., Fonseca, L. C., Chielle, R. S. A. and Macedo, L. C. B. (2018). Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis. Revista Brasileire de Recursos Hidricos Brazilian. J. Water Resour., 23, 1-12.

Asgari, M. S. (2011). Hydrochemical study of Birjand aquifer.M.Sc., Faculty of Engineering, Department of Mining Engineering, Birjand University [In Persian].

Asgharimoghaddam, A. and Adigozalpour, A. (2016). Investigation of Aluminum, Iron, Manganese, Chromium and Cadmium Concentrations in Groundwater of Oshnavieh Plain. Iran. J. Ecohydro., 3, 167-179 [In Persian].

Babaeihessar, S., Hamdami, Q. and Ghasemieh, H. (2016). Identify the Effective Wells in Determination of Groundwater Depth in Urmia Plain Using Principle Component Analysis. J. Water. Soil., 31, 10-50 [In Persian].

Bazrafshan, J. and Hejabi, S. (2017). Drought Monitoring Methods. University of Tehran Press [In Persian].

Gurunathan, K. and Ravichandran, S. (1994). Analysis of water quality data using a multivariate statistical technique- a case study. IAHS Pub, 219pp.

Farpoor, F., Ramezani, Y. and Akbarpour, A. (2019). Numerical Simulation of Chromium Changes Trend in Aquifer of Birjand plain. Iran. J. Irrig. Drain., 12(5), 1203-1216 [In Persian].

Hooshangi, N., Aleshaikh, A. A. and Nadiri, A. A. (2015). Optimization of Piezometers Number for Groundwater Level Prediction Using PCA and Geostatistical Methods. Water. Soil. Sci., 25, 53-66.

Hu, S., Luo, T. and Jing, C. (2013). Principal component analysis of fluoride geochemistry of groundwater in Shanxi and Inner Mongolia, China. J. Geo. Explor., 135, 124-129.

Ishtiyaq, N., Anisa, K. and Abdul, H. (2017). Evaluation of seasonal variability in surface water quality of Shallow Valley Lake, Kashmir, India, using multivariate statistical techniques. Pollu., 3, 349-362.

Jafarzadeh, A. and Khasheisiuki, A. (2018). Performance examination of optimization model of groundwater monitoring network based on Gray wolf and Neural network (GNM) (Case study: Birjand plain). J. Irrig. Water. Engin., 8, 121-139.

Kavusi, M., Khasheisiuki, A. and Dastourani, M. (2020). Optimal Design of Groundwater Monitoring Network Using the Combined Election-Kriging Method. Springer;European Water. Res. Asso. (EWRA), 34, 2503- 2516.

Kavusi, M., Khasheisiuki, A., Porrezabilondi, M. and Najafi, M. H. (2019). Application of New LSSVM-PSO Optimization-Simulation Model in Designing Optimal Groundwater Level Network Monitoring. Iran. J. Ecohydro., 5, 1306-1319.

Khodaverdi, M., Hashemi, S. R., Khasheisiuki, A. and Porrezabilondi, M. (2020). Optimal Design of Groundwater-Quality Sampling Networks with MOPSO-GS (Case Study: Neyshabour Plain). J. Water. Irrig. Manag. (J. Agric.), 9, 199-210 [In Persian].

Markus, M., Knapp, H. V. and Tasker, G. D. (2003). Entropy and generalized least square methods in assessment of the regional value of stream gages. J. Hydro., 283: 107-121.

Mohammadzadeh, H. and Heydarizad, M. ()2011. Hydrochemical and stable isotopes study (O18 and H2 surface and groundwater resources) Andarkh Karstic region (north of Mashhad). Earth. Sci. Res., 2, 59-69 [In Persian].

Mogheir, Y. and Singh, V. P. (2003). Specification of information needs for groundwater management planning in developing country. Groundwater Hydrology, Balema Publisher, Tokyo, 2, 3-20.

Mogheir, Y., De Lima, J. L. M. P. and Singh, V. P. (2004). Characterizing the spatial variability of groundwater quality using the entropy theory: I. Synthetic data. Hydro. Pro., 18(11), 2165-2179.

Noori, R., Abdoli, M. A., Ameri Ghasrodashti, A. and Jalili Ghazizade, M. (2009). Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: A case study of Mashhad. Enviro. Pro. Sust. Ener., 28, 249-258 [In Persian].

Noorighidari, M. H. (2013). Determintion of Effective Wells to Monitor the Ground Water Level Using the Principal Components Analysis. J. Sci. Techno. Agri. Nat. Resour., 17, 149-159 [In Persian].

Ouyang, Y. (2005). Evaluation of river water quality monitoringstations by principal component analysis. Water. Res., 39, 2621-2635.

Petersen, W. (2001). Process identification by principal component analysis of river water-quality data. Eco. Model., 138.

Rahnama, S. and Sayari, N. (2019). Survey and Trends of Chemical Water Quality Parameters of Tajan River Water Quality Using Principal Component Analysis and Aqua Chem Software. Human. Enviro., 48, 13-25 [In Persian].

Rajaei, Q., Hasanpour, M. and Mehdinejad, M. H. (2012). Heavy Metals Concentration (Zinc, Lead, Chrome and Cadmium) in Water and Sediments of Gorgan Gulf and Estuarine Gorganroud River, Iran. Health. Sys. Res., 8, 748-756 [In Persian].

Rezaei, E., Khasheisuki, A. and Shahidi, A. (2015). Design of Groundwater Level Monitoring Network, Using the Model of Least Squares Support Vector Machine (LS-SVM). Iran. J. Soil. Water. Res., 45, 389-396 [In Persian].

Sanchez- Martos, F., Jimenez- Espinosa, R. and Pulido- Bosch, A. (2001). Mapping groundwater quality variables using PCA and geostatistics: a case study of Bajo Andarax, southeastern Spain. Hydro. Sci. J., 46, 227- 242.

Shahriyari, A., Goleirozy, K. and NOSHIN, S. (2010). Muscular concentration of cadmium and lead in carp, mullet and kutum of the Gorgan Bay, Caspian Sea. Iran. Fish. Sci. Rese. Inst., 19, 95-100 [In Persian].

Shahryari, T., Moashery, B. and Sharifzadeh, G. (2011). Concentrations of chromium and copper in the ground water and drinking water distribution network of Birjand, 2009-2010. J. Birjand Univ Med Sci., 18 (1): 62-67 [In Persian].

Shannon, C. E. (1948). A mathematical theory of communication, Bell. Sys. Tech. J., 27:379-423.

Zhao, Y., Xia, X. H., Yang, Z. F. and Wang, F. (2012). Assessment of water quality in Baiyangdian Lake using multivariate statistical techniques. Proc. Enviro. Sci., 13, 1213-1226.

Environment and Water Engineering

Comparision of Birjand Plain Aquifer Chromium Monitoring Network Using Principal Component Analysis (PCA) and Entropy Theory

References

References

Volume 7, Issue 2
June 2021
Pages 220-231

Comparision of Birjand Plain Aquifer Chromium Monitoring Network Using Principal Component Analysis (PCA) and Entropy Theory

References

References

Volume 7, Issue 2June 2021Pages 220-231

Volume 7, Issue 2
June 2021
Pages 220-231