Designing Monitoring Network for Rain Gauge Stations Using Irregularity Theory (Case Study: Urmia Lake Basin)

Document Type : Original Article

Authors

1 Department of Sciences and Water Engineering, University of Birjand

2 Water engineering Dept. Faculty of Agriculture University of Birjand Birjand Iran

3 Assistant Prof of Water Engineering, Department. University of Birjand., Birjand., Iran

4 Department of Water Engineering, University of Birjand

Abstract

Designing of water quantity and quality monitoring system has been raised as one of the most complex issues in the field of water resources and the environment. Designing of these systems used to achieve qualitative and quantitative information, while their design process requires basic information. The study area in this research is Urmia Lake basin that located in the North West of Iran. Entropy literally means disorder. In this study used entropy theory to rain gaging monitoring in period of 1984-2011. Also The modified Mann - Kendal test was used to study the trend of the studied parameters The results of the study of the trend of precipitation values of Urmia Lake basin at annual scale showed that rainfall changes in this basin have been decreasing in the annual scale. In order to study and monitoring the rain gauge network using Entropy theory, two methods of support vector regression and kriging were used to estimate precipitation values. Results indicated that the accuracy of the regression model was higher than the Kriging model. The results of the evaluation of the entropy index at the aquifer showed that only 1.4% of the studied basin had a severe shortage of information that required the construction of a new station in the area. However, since more than 90 percent of the basin area is in terms of data transmission in excess and relatively excessive condition, the study area is relatively good at its monitoring. In general, the results indicated that the accuracy of the optimized method of support vector regression was used to estimate the annual rainfall in the Urmia Lake basin. The results of the stations' ranking in the study area showed that the stations of Jharabad, Badamlou and Orban received ratings ranging from 1 to 3, which indicates the transfer and reception of more information than other stations.

Keywords


اکبرزاده، م.، قهرمان، ب. 1392. استفاده هم‌زمان از آنتروپی و کریجینگ فضایی- زمانی برای تعیین شبکه بهینه پایش کیفی منابع آب زیرزمینی دشت مشهد، نشریه آب و خاک. جلد 27. شماره 3. صفحات 629-613.
فانی، مرتضی.، خلیفه، س.، خلیفه، ا.، افلاطونی، م. 1394. ارزیابی شبکه ایستگاه­های باران­سنجی با استفاده از تئوری آنتروپی گسسته (مطالعه موردی: حوضه آبریز کارون بزرگ. علوم و مهندسی آبیاری، 38(4): 13-1.
فرجی، ح.، محمودی میمند، ه.، نظیف، س.، عباسپور، ر. 1393. توسعه بهینه شبکه باران‌سنجی با استفاده از کریجینگ و آنتروپی در محیط GIS (مطالعه موردی: حوضه آبریز کرخه). پژوهش­های جغرافیای طبیعی. 46(4): 462-445.
قهرمانی، ع.، شقاقیان، م. 1394. طراحی شبکه باران با استفاده از مفهوم آنتروپی (مطالعه موردی: حوضه آبریز کرخه). دهمین کنگره بین المللی مهندسی عمران. دانشکده مهندسی عمران. دانشگاه تبریز.
معصومی، ف.، کراچیان، ر. 1387. بهینه­سازی مکان‌یابی ایستگاه‌های پایش کیفی منابع آب زیرزمینی با استفاده از تئوری آنتروپی. مجله آب و فاضلاب. 65: 12-2.
مهجوری­مجد، ن.، کراچیان، ر. 1387. ارزیابی کارایی سیستم­های پایش کیفی رودخانه با استفاده از تئوری آنتروپی گسسته )رودخانه جاجرود). دومین همایش و نمایشگاه تخصصی مهندسی محیط­زیست، دانشگاه تهران، تهران.
میرعباسی، ر.، دین­پژوه، ی. 1387. ارزیابی شبکه پایش کیفیت دشت اهر براساس تئوری آنتروپی، اولین همایش ملی مدیریت منابع آب اراضی ساحلی، دانشکده علوم کشاورزی و منابع طبیعی، ساری.
Chadalavada,S., Datta,B., Naidu, R. 2011. Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site. Environmental monitoring and assessment. 173.1-4: 929-940.
Chen,Y.C., Wei,C., Yeh,H.C. 2008. Rainfall network design using kriging and entropy. Hydrological Processes. 22. 3: 340.
Dorigo,M., Maniezzo,V., Colorni,A. 1991. The ant system: An autocatalytic optimizing process.
Hamed,K.H., Rao,A.R. 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology. 204.1-4: 182-196.
Harmancioglu,N.B., Alpaslan,N. 1992.  Water quality monitoring network design: A problem of multiobjective decision making . JAWRA Journal of the American Water Resources Association. 28.1: 179-192.
Jaynes,E.T. 1957. Information theory and statistical mechanics. Physical review. 106.4: 620– 630.
Kawachi,T., Maruyama,T., Singh,V.P. 2001. Rainfall entropy for delineation of water resources zones in Japan. Journal of Hydrology. 246.1: 36-44.
Kendall,M.G. 1975. Rank Correlation Measures. Charles Griffin. London.
Khalili,K., Tahoudi,M.N., Mirabbasi,R., Ahmadi,F. 2016. Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stochastic Environmental Research and Risk Assessment. 30.4: 1205-1221.
Kumar,S., Merwade,V., Kam,J., Thurner,K. 2009. Streamflow trends in Indiana: effects of long term persistence, precipitation and subsurface drains. Journal of Hydrology. 374.1: 171-183.
Lubbe,C. 1996. Information Theory, Cambridge: Cambridge University Press.
Mann,H.B. 1945. Nonparametric tests against trend. Econometrica: Journal of the Econometric Society. 245-259.
Markus,M., Knapp,H.V., Tasker,G.D. 2003. Entropy and generalized least square methods in assessment of the regional value of streamgages. Journal of hydrology. 283.1: 107-121.
Mishra,A.K., Coulibaly,P. 2010. Hydrometric network evaluation for Canadian watersheds. Journal of Hydrology. 380.3: 420-437.
Mogheir,Y., Singh,V.P. 2002. Application of information theory to groundwater quality monitoring networks. Water Resources Management. 16.1: 37-49.
Mogheir,Y., Singh,V.P. 2003. Specification of information needs for groundwater management planning in developing country. Groundwater Hydrology. Balema Publisher, Tokyo. 2: 3-20.
Nash, J.E., Sutcliffe, J.V. 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of hydrology, 10(3): 282-290.
Nunes,L.M., Cunha,M.C., Ribeiro,L. 2004. Groundwater monitoring network optimization with redundancy reduction. Journal of Water Resources Planning and Management. 130.1: 33-43.
Ozkul,S., Harmancioglu,N.B., Singh,V.P. 2000. Entropy-based assessment of water quality monitoring networks. Journal of hydrologic engineering. 5.1: 90-100.
ŞARLAK,N. 2005. Evaluation and modeling of sreamflow data: Entropy method, Autoregressive models with asymmetric innovation and artificial neural networks (Doctoral dissertation, MIDDLE EAST TECHNICAL UNIVERSITY).
Sen,P.K. 1968. Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association. 63.324: 1379-1389.
Shannon,C.E. 1948. A mathematical theory of communication, bell System technical Journal 27: 379-423 and 623–656. Mathematical Reviews (MathSciNet): MR10, 133e.
Socha,K., Dorigo,M. 2008. Ant colony optimization for continuous domains. European journal of operational research. 185.3: 1155-1173.
Stützle,T., Hoos,H.H. 2000. MAX–MIN ant system. Future generation computer systems. 16.8: 889-914.
Theil,H. 1950. A rank-invariant method of linear and polynomial regression analysis, 3; confidence regions for the parameters of polynomial regression equations. Stichting Mathematisch Centrum. Statistische Afdeling, (SP 5a/50/R). 1-16.
Vapnik,V. 1998. Statistical learning theory. 1998. Wiley, New York.
Zhu,Q., Shen,L., Liu,P., Zhao,Y., Yang,Y., Huang,D. Yang,J. 2015. Evolution of the Water Resources System Based on Synergetic and Entropy Theory. Polish Journal of Environmental Studies. 24.6: 2727-2738.