Estimation Of Average Annual Precipitation Of Khorasan Razavi Province Using Spatial Coordinates

Document Type : Original Article

Authors

1 Ph.D. Candidate of Irrigation and Drainage, International Branch, Ferdowsi University of Mashhad., Mashhad., Iran

2 Professor,Water Engineering Department, College of Agricultural, Ferdowsi University of Mashhad., Mashhad., Iran

Abstract

Prediction of annual precipitation is of prime importance for water resources management and agricultural crop pattern planning .Different methods are used such as classical estimation method, geostatistical methods, multiple linear regression, artificial neural networks and Kriging combined with neural networks for prediction of precipitation at unsampled locations.In this study, non-climatic inputs, were used such as geographic coordinates and altitude, and average annual precipitation of 47 stations (corresponding to 25 years period of 1886 to 2011)  at province of Khorasan Razavi were adopted. The highest correlation was found to be between average annual precipitation (as dependent variable) and three coordinate variables of altitude and geographical coordinates of the stations (as independent variables). Based on the results, however, IDW method was not sensitive to altitude. Effective range was 30.2 kilometers under SK and OK methods, while it was increased to 40 kilometers under ordinary cokriging which supports for hight dependency of precipitation to altitude in spatial context. The semivariogram parameters of sill, nugget effect, effective range and relative nugget effect were quite different for different years which is an indication of spatio-temporal pattern of precipitation at the study area. The best results were attributed to RK and SKV, however. The structure of the adopted neural network in this study was the multilayer perceptron (MLP) with sigmoid tangent and linear functions. Different structures of neural networks were tested with different inputs, the optimum network for prediction of average annual precipitation was attributed to three inputs (longitude, latitude and altitude) corresponding to 3-6-1 structure with Levenberg-Marquardt algorithm. The highest correlation coefficient and the lowest error were due to artificial neural network method, so it was the best method in this study.

Keywords

References

انصاری، ح و داوری، ک. 1389. تخمین و بازسازی داده­های بارندگی با تکنیک فازی، مجله تحقیقات منابع آب ایران، سال ششم، شماره1، صفحه 39-47.
پولادی،ا. 1381. مقایسه عملکرد شبکه عصبی مصنوعی با سایر روش­ها در برآورد مکانی بارندگی روزانه، پایان نامه کارشناسی ارشد، دانشگاه شیراز، گروه عمران.
پیکتن،ف. 1388. شبکه­های عصبی، ترجمه میرصالحی،م.م وتقی­زاده کاخکی،ح. انتشارات دانشگاه فردوسی مشهد، 280 صفحه.
حسنی پاک،ع.ا. 1389. زمین­آمار (ژئواستاتیستیک). انتشارات دانشگاه تهران، 314 صفحه.
عساکره،ح. 1384. تغییرات زمانی-  مکانی بارش استان اصفهان طی دهه­های اخیر، مجله پژوهشی دانشگاه اصفهان (علوم انسانی). جلد 18 ، شماره ۱، صفحه 91-116.
کریمی گوغری،ش واسلامی،ا. 1387. پیش­بینی بارندگی سالانه در استان کرمان با استفاده از شبکه­های عصبی مصنوعی. مجله آبیاری و زهکشی ایران، جلد 2، شماره 2، صفحه 123-132.
مدنی،ح. 1373. مبانی زمین آمار، انتشارات دانشگاه صنعتی امیرکبیر واحد تفرش، 658 صفحه.
مصداقی،م. 1390. روش­های آمار و رگرسیون با رویکرد کاربردی در علوم گیاهی و جانوری، انتشارات دانشگاه امام رضا (ع) مشهد، 421 صفحه.
مهدوی،م. 1377. هیدرولوژی کاربردی، جلد اول، انتشارات دانشگاه تهران.
مهدی زاده،م.، مهدیان،م.ح و حجام،س. 1385. کارایی روش­های زمین آماری در پهنه­بندی اقلیمی حوزه آبریز دریاچه ارومیه. مجله فیزیک زمین و فضا.32: 116-103.
Azadi,S and Sepaskhah,A.R. 2012. Annual precipitation forecast for west،southwest، and south provinces of Iran using artificial neural networks, Theoretical and Applied Climatology,109:175-189.
Basist,A. Bell,G.D and Meentemeyer,V. 1994. Statistical relationships between topography and precipitation patterns.Journal of Climate,7(9), 1305–1315.
Chang,C.L., Lo,S.L and Yu,S.L. 2005. Applying fuzzy theory and genetic algorithm to interpolate precipitation.Journal of Hydrology, 314: 92-104.
Daly,C., Neilson,R.P and Phillips,D.L. 1994. A Statistical-Topofraphic Model for Mapping Climatological Precipitation over Mountainous Terrain, Journal of Applied Meteorology, 33, 140-158.
Demuth,H and Beal,M. 2002. Neural Network Toolbox For Use with MATLAB. User’s Guide, Version 4. The MathWorks, 840pp.
Demyanov,V., Kanevsky,M., Chernov,S., Savelieva,E and Timonin,V. 1998. Neural Network residual kriging application for climate data.Journal of Geographic Information and Decision Analysis. 2, 215-232.
Diodato,N and Ceccarelli,M. 2005. Interpolation processes using multivariate geostatistics for mapping of climatological precipitation mean in the  Sannio  Mountains (southern Italy), Earth Surface Processes and Landforms.30:259-268.
Di Piazza,A., Lo Conti,F., Noto,L.V., Viola,F and La Loggia,G. 2011. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy, International Journal of Applied Earth Observation and Geoinformation .13. 396–408
Diskin,M.H. 1970. Factors Affecting Variation of Mean Annual Rainfall in Israel. Bulletin of the International Association of Scientific Hydrology. XV. 4: 41-49
El-Shafie,A.H., El-Shafie,A., El Mazoghi,H.G., Shehata,A and Taha,M.R. 2011. Artificial neural network technique for rainfall forecasting applied to Alexandria، Egypt، International Journal of the Physical Sciences Vol. 6(6). 1306-1316.
French,M.N., Krajewski,W.F and Cuykendall,R.R. 1992. rainfall forecasting in space and time using neural network, Journal of Hydrology, 137,1-31.
Gadgay,B., Kulkarni,S and Chandrasekhar,B. 2012. Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach, International Journal of Computer Applications (0975 – 8887) Volume 39– No.18, pp:37-45.
Goodale,C.L., Alber,J.D and Ollinger,S.V. 1998. Mapping monthly precipitation، temperature and solar radiation for Ireland with polynomial regression and digital elevation model.Climate Research.10, 35–49.
Goovaerts,P. 2000. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology. 228, 113–129.
Kim,T and Valdes,J.B. 2003. Nonlinear model for drought forecasting based on conjunction of wavelet transforms and neural networks،Journal of Hydrologic Engineering.ASCE,Volume8(6), pp:319-328.
Kravchenko,A., Zhang,R and Tung,Y.K. 1996. Estimation of Mean Annual Precipitation inWyoming Using Geostatistical Analysis.1996. Proceedings of American Geophysical Union،16 thAnnual Hydrology Days، Fort Collins, Colorado, pp. 271-282.
Lo,S.S. 1992. Glossary of Hydrology, Water Resources Publications. PP 1794
Luc,K.C., Ball,J.E and Sharma,A. 2001. An Application of artificial neural networks for forecasting, Mathematical and Computer Modelling, 33 (6-7), pp:683-693.
Luk,K.C., Ball,J.E and Sharma,A. 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting, Journal of Hydrology, 227, 56-65.
Maier,H.R and Dandy,G.C. 2000. Application of artificial neural networks to forecasting of surface water quality variables: Issues, applications and challenges. In: Artificial neural networks in hydrology. 287-309.
Martinez-Cob,A. 1996. Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain. Journal of Hydrology. 174 (1–2)، 19–35.
Moral,F.J. 2010. Comparison of different geostatistical approaches to map climate variables: application to precipitation, Internatinal Journal of Climatology, 30:620-631.
Namasivayam,A and Waldemar,A. 2001. Comparison of prediction of extremely low birth weight neonatal mortality by regression analysis and by neural networks, Early human Development, 65, 123-137.
Ninyerola,M., Pons,X., Roure,J.M. 2000. A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. International Journal of Climatology. 20 (14), 1823–1841.
Pardo-Iguzquiza,E. 1998. Comparison of Geostatistical Methods for Estimating the Areal Average Climatological Rainfall Mean Using Data on Precipitation and Topography،International Journal of Climatology, 18:1031-1047.
Prudhomme,C and Duncan, W.R. 1999. Mapping extreme rainfall in a mountainous region using geostastistical techniques: a case study in Scotland. International Journal of Climatology. 19 (12)، 1337–1356.
Ramirez,M.C.V., Velho,H.F.D.C and Ferreira,N.J. 2005. Artificial neural network technique for rainfall forecasting applied to the Saopaulo region. Journal of hydrology. 301 (1-4 ). pp:146-162.
SeyyednezhadGolkhatmi,N., Sanaeinejad,S.H., Ghahraman,B and Rezaee Pazhand,H. 2012. Extended Modified Inverse Distance Method for Interpolation Rainfall, International Journal of Engineering Inventions, 1(3):57-65.
Smith,R.B. 1979. The influence of mountains on the atmosphere.Adv. Geophys. 21, 87–230.Academic Press.
Spreen,W.C. 1947. A determination of the effect of topography upon precipitation. Trans. Am. Geophys. Union 28, 285–290.
Tabios,G.Q and Salas,J.D. 1985. A Comparative Analysis of Techniques for Spatial Analysis Precipitation.Water Resources Bulletin.21:365-380.
Tssung,C.K. 2004. Introduction to geographic information system, 2th edition, Mc Graw Hill.
Valipour,M. 2012. Critical Areas of Iran for Agriculture water management According to the Annual Rainfall, European Journal of Scientific Research, ISSN 1450-216X, 84 (4 ):600-608.
Weisse,A.K and Bois,P. 2001. Topographic effects on statistical characteristics of heavy rainfall and mapping in the French Alps. Journal of Applied Meteorology. 40 (4), 720–740.
Wotling,G., Bouvier,Ch., Danloux,J and Fritsch,J.M. 2000. Regionalization of extreme precipitation distribution using the principal components of the topographical environment. Journal of Hydrology. 233, 86–101.
 
Iranian Journal of Irrigation & Drainage
Volume 8, Issue 2 - Serial Number 46
November and December 2014
Pages 205-219

Files

Share

How to cite

Statistics