Designing a network for monitoring groundwater level using the Principal Component Analysis technique

Subject Areas : Article frome a thesis

A. SAYADI ¹ , abdali naseri ² , saeed boromandnasab ³ , amir soltani ⁴

1 - دانشجوی دکترای آبیاری و زهکشی دانشگاه شهید چمران اهواز، اهواز، ایران
2 - Professor
3 - Professor
4 - Associate Professor

Received: 2018-10-28 Accepted : 2020-05-12 Published : 2020-04-20

Keywords: Groundwater, Principal Component Analysis, Monitoring,

Abstract :

Well designed monitoring networks are essential for the effective management of groundwater resources but the costs of monitoring well installations and sampling can prove prohibitive. Principal Component Analysis (PCA) is one of the data reduction techniques used to extract the important components that explain the variance of a system. In this paper, the PCA was used to identify the effective wells and remove the less important ones. For this purpose, 160 wells were constructed in the Salman Farsi agro-industry, which was measured twice in a month during 10 months. In this technique, variation factors called principle components are identified with considering data structures. Using the PCA, the relative importance of each well was calculated for groundwater depth estimation. In the present study, the acceptable threshold was taken to be 0.8, according to which the number of wells in determining groundwater depth was reduced to 33 wells. By identifying important wells, important points for sampling are identified, and groundwater depth monitoring is performed only in these wells. As a result, it can save a great deal of time and cost of studies.

References:

1)       شناسایی چاه های مؤثر در تعیین عمق آب زیرزمینی دشت ارومیه با استفاده از آنالیز مؤلفه های اصلی. نشریه آب و خاک. 30 (1): 40-50.

2)       نوری قیداری، م.ح. 1392. تعیین چاه­های مؤثر در تعیین تراز سطح آب زیرزمینی با آنالیز مؤلفه­های اصلی. مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک. 17 (64): 149-158.

3)        Aadil, N., Adrian H. and Shakeel, A. 2011. Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India. Sustainability. 3:396-409.

4)        Debels P., Figueroa R., Urrutia R., Barra R., and Niell X. 2005. Evaluation of water quality in the Chilia River using Physicochemical parameters and a modified water quality index. Environmental Monitoring and Assess, 110:L 301–322.

5)        Gurunathan, K. and S. Ravichandran. 1994. Analysis of water quality data using a multivariate statistical technique -a case study. IAHS Pub., No. 219.

6)        Hu S., Luo T., and Jing C. 2013. Principal component analysis of fluoride geochemistry of groundwater in Shanxi and Inner Mongolia, China. Journal of Geochemical Exploration, 135: 124–129.

7)        Helena B., Pardop R., Vega M., Barrado E., Manuel J., and Fernandez L. 2000. Temporal evolution of groundwater composition in an alluvial aquifer by principal component analysis. Water Resource, 34(3): 807-816.

8)        Jolliffe i.t. 2002. Principal Component Analysis. Springer series in statics, ISBN 978-0-387-95442-4.

9)        Lucas, L. and M. Jauzein. 2008. Use of principal component analysis to profile temporal and spatial variations of chlorinated solvent concentration in groundwater. Environ. Pollut.151: 205-212.

10)     Nguyan T.T., Nakagawa A.K., Amaaguchi H., and Gilbuena R. 2013. Temporal chenges in the hydrochemical facies of groundwater quality in tow main aquifers in Hanoi. Vietnam, DOI: 10.5675/ICWRER_2013.

11)     Pearson K. 1901. On lines and plans of closest fit to systems of points in Space. Philosophical Magazine 2(6): 559-572.

12)     Petersen, W. 2001. Process identification by principal component analysis of river water-quality data. Ecol. Model.138: 193-213.

13)     Sauquet, E. 2000. Mapping mean monthly runoff pattern using EOF analysis. Hydrol. and Earth Sys. Sci. 4(1):79-93.