ارزیابی و مقایسه روش‌های درون‌یابی و رگرسیون خطی جهت تعیین توزیع مکانی بارش در استان چهارمحال و بختیاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی طبیعت، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران

2 دانش آموخته دکترا، گروه علوم و مهندسی آبخیزداری، دانشکده کشاورزی، دانشگاه یزد، یزد، ایران

چکیده

در پژوهش حاضر، روش­های کریجینگ ساده و معمولی، معکوس فاصله و رگرسیون خطی بر پایه مدل ارتفاعی رقومی زمین، جهت برآورد بارش سالانه با استفاده از آمار بیست ساله­ داده­های بارش (2018– 1998) در 33 ایستگاه باران­سنجی استان چهارمحال و بختیاری مورد ارزیابی قرار گرفت. بدین منظور، ابتدا در ArcMAP به ازای هر مدل در روش کریجینگ، واریوگرام آن محاسبه و با استفاده از فن ارزیابی دوجانبه، خطای نقشه­ها برآورد شد. بهترین روش از میان روش­های زمین‌آماری، روش کریجینگ معمولی با مدل گوسی بود؛ شاخص­های آماری MAE، MBE و RMSE  برای این روش به­ترتیب 44/74، 48/0 و 72/93 به­دست آمد. سپس داده­های بارش و ارتفاع ایستگاه­های موردنظر با استفاده از مدل رگرسیون خطی در محیط Curve Expert فراخوانی گردید. درنهایت به‌منظور تعیین بهترین مدل برای توزیع مکانی بارش و هم­چنین مقایسه روش­های آماری و زمین‌آماری، مدل­های رگرسیون خطی و کریجینگ معمولی با فن ارزیابی متقابل با یکدیگر مقایسه شدند و شاخص­های آماری MAE، MBE و RMSE برای روش رگرسیون خطی به­ترتیب 115، 3 و 155 به­دست آمد. درنتیجه با توجه به‌دقت، صحت و میزان خطای نقشه­های تهیه‌شده مناسب­ترین روش برای درون­یابی بارش سالانه روش کریجینگ معمولی با مدل گوسی می­باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation and Comparison of Interpolation and Linear Regression Methods to Determine the Spatial Distribution of Precipitation in Chaharmahal and Bakhtiari Province, Iran

نویسندگان [English]

  • Farzaneh Fotouhi Firoozabad 1
  • Hamideh Afkhami Ardakani 2
1 Assist. Professor, Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, Ardakan, Iran
2 PhD Alumni, Department of Watershed Management Engineering, Faculty of Natural Resources and Desert, Yazd University, Yazd, Iran
چکیده [English]

In the present study, simple and ordinary kriging methods, inverse distance and linear regression based on digital elevation model of the earth were evaluated for estimating annual rainfall using twenty-year statistics of precipitation data (1998-2018) in 33 rainfall stations in Chaharmahal and Bakhtiari province. For this purpose, first in ArcMAP, for each model in Kriging method, its variogram was calculated and using two-way evaluation technique, the error of the maps was estimated. The best method among geostatistical methods was conventional kriging method with Gaussian model. MAE, MBE and RMSE statistical indices for this method were 74.44, 0.48 and 93.72, respectively. Then, rainfall and altitude data of the stations were used using a linear regression model in Curve Expert environment. Finally, in order to determine the best model for spatial distribution of precipitation as well as comparing statistical and geostatistical methods, linear regression and ordinary kriging models were compared with each other and the MAE, MBE and RMSE statistical indices for regression method obtained were 115, 3 and 155, respectively. As a result, due to the accuracy, precision and error rate of the prepared maps, the most suitable method for interpolation of annual precipitation is the conventional kriging method with Gaussian model.

کلیدواژه‌ها [English]

  • Cross validation
  • Geographic information system
  • Kriging
  • Digital Elevation Model
  • Inverse distance weighting
Alijani, B. (2004). Iran's climate. Tehran: Payame noor University Publications, 230 pp [In Persian].
Alijani, B. and Kavyani, M. (2019). Principles of climatology. Tehran: Samt Pub., 600 pp [In Persian].
Allexandra, K. and Bullock, D. G. (1999). A comparative study of interpolation methods for mapping soil properties, Agron J., 91(3), 393-400.
Alsafadi, K., Mohammed, S., Mokhtar, A., Sharaf, M. and He, H. (2021). Fine-resolution precipitation mapping over Syria using local regression and spatial interpolation. Atmos Res. 256: 105524. Doi: 10.1016/j.atmosres.2021.105524.
Asakereh, H. (2009). Kriging application in climatic element interpolation (a case study: Iran precipitation in 1996.12.16). Geogra. Develop., 6(12), 25-42 [In Persian].
Faraji Sabokbar, H. A. and Azizi, G. (2007). Assessing the accuracy of spatial interpolation methods Case study: Rainfall modeling in Kardeh basin of Mashhad. Geogra. Res., 38(6), 1-15 [In Persian].
Ghahroodi Tali, M. (2005). Geographic information system in three-dimensional environment: Jahad Daneshgahi Vahed Tarbiat Moalem Pub., 273 pp [In Persian].
Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J. Hydrol. 22(8), 113-129.
Hamidiyanpour, M., Saligeh, M. and Falah, G. (2013). Applying types of interpolation methods for spatial analysis and monitoring of SPI drought case study: Khorasan Razavi Province. Geogra. Develop., 11(30), 57-70 [In Persian].
Hargrove, W.W. (2008). Interpolation of rainfall in Switzerland using a regularized spline with tension. Accessed on June 17, 2021. Available at: https://www.geobabble.org/~hnw/sic97/.
Hu, D., Shu, H., Hu, H. and Xu, J. (2017). Spatiotemporal regression Kriging to predict precipitation using time-series MODIS data. Cluster Comput. 20(1), 347-357.
Jafarpoor, A. (2020). Climatology. Tehran: Tehran University Pub., 253 pp [In Persian].
Journel, A. G. (1989). In: Fundamental of Geostatistics in Five Lesson, Short Course in Geology, Short Course Presented at the 28th International Geological Congress Washington, D.C. Vol 8. American Geophysical Union.
Kalantari, K. (2007). Data analysis in socio-economic research using SPSS software. Sharif Pub., 361 pp [In Persian].
Kravchenko, A. N. (2003). Influence of spatial structure on accuracy of interpolation methods. Soil Sci. Soc. Am J., 67(5), 1564-1571.
Kumari, M., Singh, C. K., Bakimchandra, O. and Basistha. A. (2017). DEM-based delineation for improving geostatistical interpolation of rainfall in mountainous region of Central Himalayas, India. Theor. Appl. Climatol. 130 (1-2), 51-58.
Kumari, M., Singh, C. K., Oinam, B. and Basisthae, A. (2016). Geographically weighted regression-based quantification of rainfall–topography relationship and rainfall gradient in Central Himalayas. Int. J. Climatol., 37(3), 1-12.
Liu, D., Zhao, Q., Fu, D., Guo, Sh., Liu, P. and  Zeng, Y. (2020). Comparison of spatial interpolation methods for the estimation of precipitation patterns at different time scales to improve the accuracy of discharge simulations. Hydrol. Res., 51(4), 583–601.
Mahdavi, M. (2011). Applied hydrology. Tehran University Pub., 442 pp [In Persian].
Mahdavi, M., Hosseini Chegini, A., Mahdian, M. H. and Rahimi Bondarabadi, S. (2004). Application of geostatistical methods for estimation of annual spatial rainfall in arid and semiarid regions of south-east of Iran. Iran. J. Nat. Resour., 57(2), 211-224 [In Persian].
Mahdian, M. H., Ghiasi, N. Gh. and Mousavy Nejad, S. M. (2003). Investigation of appropriate special interpolation methods for estimating monthly rainfall data in central Iran. J. Water Soil Sci., 7(1), 33-45 [In Persian].
Mahdizadeh, M., Mahdian, M. H. and Hejam, S. (2006). Efficiency of geostatistical methods in climatic zoning of lake Urmia Basin. J. Earth Space Phys., 32(1), 103-116 [In Persian].
Mahmoudvand, S., Khodayari, H. and Tarnian, F. (2020). Mapping bioclimatic variables using geostatistical and regression techniques in Lorestan Province. J. Geogra. Stud. Mountain. Area., 1(3), 1-17 [In Persian].
Mehrshahi, D. and Khosravi, Y. (2010). Evaluation of kriging and linear regression interpolation methods based on digital elevation model to determine the spatial distribution of annual rainfall (Case study of Isfahan province). J. Spat. Plan., 14(4), 233-249 [In Persian].
Misaghi, P. and Mohamadi, K. (2006). Zonation rainfall data using classical statistics and geostatistics and compared with artificial neural networks. Sci. J. Agri., 29(4), 1-14 [In Persian].
Mohammadi, J. (2007). Pedometry. Spatial Statistics, Pelk Pub., 453 pp [In Persian].
Mohammadi, J. and Motaghian, M. H. (2011). Spatial prediction of soil aggregate stability and aggregate-associated organic carbon content at the catchment scale using geostatistical techniques. Pedosphere. 21(3), 389-399 [In Persian].
Mojarrad, F. and Kakaee, H. (2015). Application of Interpolation and Regression Methods in Spatial Estimation of Rainfall (Case Study: Kermanshah Province). Geogra. Plan. Space. 5(16), 181-197 [In Persian].
Mozafari, Gh. A., Mirmusavi, S. H. and Khosravi, Y. (2012). The Assessment geostatistics methods and linear regression in order to specify the spatial distribution of annual precipitation (case study: Boushehr province). Geogra. Develop., 10(27), 63-76 [In Persian].
Naoum, S. and Tsanis, I. K. (2004). Ranking spatial interpolation techniques using a GIS-based DSS. Glob. Nest. J., 6(1), 1-20.
Ninyerola, M., Pons, X. and Roure, J.M. (2007). Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System. Theor. Appl. Climatol. 89(3-4), 195-209.
Podineh, O., Delbari, M., Haghighatjou, P. and Amiri, M. (2016). Spatial analysis of Precipitation with Elevation and Distance to Sea (Case Study: Sistan and Baluchestan Province). Physical Geogra. Res., 47(4), 607-636 [In Persian].
Rahimi Bondarabadi, S. and Sagafian, B. (2007). Estimating spatial distribution of rainfall by fuzzy set theory. Iran Water Resour. Res., 3(2), 26-38 [In Persian].
Reinstorf, F., Binder, M., Schirmer, M., Grimm-Strele, J. and Walther, W. (2005). Comparative assessment of regionalization methods of monitored atmospheric deposition loads. Atmos. Environ., 39(20), 3661-3674.
Saghafian, B., Ramzkhah, H. and Ghermez Cheshmeh, B. (2011). Investigation on regional variations of annual precipitation using geostatistical methods (case study: Fars Province). Water Resour. Eng., 4(9), 29-38 [In Persian].
Sari Sarraf, B. and Azarm, K. (2017). Estimating spatial variation of precipitation in the central Zagros using interpolation methods. J. Geogra. Not., 8(15), 74-93 [In Persian].
Shamsadini, A. (2000). Regional precipitation changes using the kriging method in the northern provinces. Master of Science in Irrigation and Drainage. Faculty of Agriculture, Shiraz University [In Persian].
Shirazi, H. and Eslami, H. (2018). Investigation of distribution spatial distribution by final interpolation methods and statistical grounds (case study: Isfahan Province). J. Water Eng., 6(2), 144-154 [In Persian].
Silva, A. S. A. D., Stosic, B., Menezes, R. S. C. and Singh, V. P. (2019). Comparison of interpolation methods for spatial distribution of monthly precipitation in the State of Pernambuco, Brazil. J. Hydrol. Eng., 24(3), 04018068.
Sun, W., Minasny, B. and McBratney, A. (2012). Analysis and prediction of soil properties using local regression-kriging. Geoderma. 171-172, 16-23.
Tsakiris, G. and Vangelis, H. (2004). Towards a drought watch system based on spatial SPI. Water Resour. Manag., 18 (1), 1–12.
Wakernagel, H. (2002). Multivariate geostatistics. Springer Press, 387 PP.
Webster, R. and Oliver, M. A. (2007). Geostatistics for environmental scientists. New York: Wiley. 2nd Edition, 336 pp.
Zabihi, A., Solaimani, K., Shabani, M. and Abravsh, S. (2012). An investigation of annual rainfall spatial distribution using geostatistical methods (a case study: Qom Province). Phys. Geogra. Res., 43(78), 102-112 [In Persian].
Zhang, G., Tian, G., Cai, D., Bai, R. and  Tong, J. (2021). Merging radar and rain gauge data by using spatial–temporal local weighted linear regression kriging for quantitative precipitation estimation.  J. Hydrol. 601, 126612. Doi: 10.1016/j.jhydrol.2021.126612
دوره 8، شماره 1
فروردین 1401
صفحه 218-232
  • تاریخ دریافت: 31 خرداد 1400
  • تاریخ بازنگری: 28 شهریور 1400
  • تاریخ پذیرش: 28 شهریور 1400
  • تاریخ اولین انتشار: 29 شهریور 1400