با همکاری انجمن آبخیزداری ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی منابع آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه ارومیه

2 استادیار، دانشکده کشاورزی و منابع طبیعی، دانشگاه ارومیه

3 استاد، دانشکده کشاورزی و منابع طبیعی، دانشگاه ارومیه

4 استادیار، دانشکده مهندسی عمران، دانشگاه صنعتی ارومیه

چکیده

آگاهی از وضعیت خشکسالی و پیش‌­بینی شرایط آتی آن نقش مهمی در برنامه‌­های مدیریت منابع آب بر­عهده دارد و در این راستا متغیرهای بارش و دما تأثیر به‌­سزایی در شدت و مدت وقوع این پدیده ایفا می‌­کنند. با توجه به وضعیت حاکم بر دریاچه ارومیه در سال­‌های اخیر و تنش آبی موجود در حوزه آبخیز آن، در این پژوهش، وضعیت خشکسالی در ایستگاه سینوپتیک سقز به‌­عنوان یکی از ایستگاه­‌های مهم جنوبی حوزه آبخیز این دریاچه در مقیاس­‌های زمانی مختلف با استفاده از شاخص بارش-تبخیر و تعرق استاندارد شده (SPEI) و مدل ماشین بردار پشتیبان (SVM) با سه تابع هسته‌­ای خطی، چند جمله­‌ای و پایه شعاعی و شبکه بیزین (BN) مورد بررسی قرار گرفت. برای این منظور از شاخص SPEI در مقیاس­‌های زمانی کوتاه­‌مدت یک و سه ماهه، میان­‌مدت شش و 12 ماهه و بلندمدت 24 و 48 ماهه در طی دوره­ آماری 49 ساله برای پایش وضعیت خشکسالی در این ایستگاه استفاده شد. نتایج نشان داد، هشت دوره طولانی مدت خشکسالی مربوط به سال‌­های 1968-1962، 1974-1972، 1979-1978، 1982-1980، 1984-1983، 1987-1986، 2003-1999 و 2009-2007 در طول دوره آماری وجود دارد. سپس، با استفاده از سری زمانی مقادیر SPEI در پنج مدل ورودی با تأخیرهای یک تا پنج ماهه و مدل­‌های SVM و BN نسبت به پیش­‌بینی خشکسالی اقدام شد. نتایج نشان داد که در هر دو روش، مدل با پنج تأخیر زمانی عملکرد بهتری داشته و تابع هسته‌­ای خطی در روش SVM نسبت به دو تابع دیگر دقت بیشتری داشته است. همچنین، دقت پیش‌­بینی­ این مدل­‌ها با افزایش مقیاس محاسبه SPEI رابطه مستقیم دارد، به‌­نحوی که ضریب همبستگی در روش شبکه بیزین در مرحله آزمون از 0.174 در مقیاس یک ماهه به 0.985 در مقیاس 48 ماهه و در روش SVM با تابع هسته­‌ای خطی نیز از 0.149 به 0.983 رسیده است.

کلیدواژه‌ها

عنوان مقاله [English]

Application of support vector machine and bayesian network for agricultural drought prediction

نویسندگان [English]

  • Abbas Abbasi 1
  • Keivan Khalili 2
  • Javad Behmanesh 3
  • Akbar Shirzad 4

1 PhD Students, Faculty of Agriculture, Urmia University, Urmia, Iran

2 Assistant Professor, Faculty of Agriculture, Urmia University, Urmia, Iran

3 Professor, Faculty of Agriculture, Urmia University, Urmia, Iran

4 Assistant Professor, Faculty of Civil Engineering, Urmia University of Technology, Urmia, Iran

چکیده [English]

Awareness of the drought status and the prediction of its future conditions play an important role in water resources management programs. In this regard, rainfall and temperature variables have a great influence on the severity and duration of this phenomenon. Regarding the status of the Urmia Lake in recent years and the water stress in its watershed, in this study, the drought situation in Saghez synoptic station as one of the important stations of this basin in different time-scales using the Standardized Evapotranspiration Index (SPEI) and SVM model with three linear, polynomial, and radial basis function and Bayesian network (BN) models, were investigated. For this purpose, the SPEI index in the short-term (1 and 3 months), mid-term (6, 12-months) and long-term (24 and 48-months) during the 49-year statistical period for monitoring the drought status at this station was used. Results showed that there was 8 prolonged periods of drought for the years 1962-1968, 1972-1974, 1978-1979, 1980-1982, 1983-1984, 1986-1987, 1999-2003 and 2007-2009 during the statistical period. Then SPEI values were applied to five input models with a delay of 1 to 5 months and SVM and BN models were used to predict drought. The results showed that in both methods, the model with 5-time delay had better performance and the linear basic function in the SVM method was more accurate than the other two functions. Also, the predictive accuracy of these models is directly correlated with increasing the SPEI scale, so that the correlation coefficient in the Bayesian network method at the test stage ranged from 0.174 in 1-month time-scale to 0.985 on a 48-month time-scale and in the SVM method with a linear basic function, it has risen from 1.149 to 0.983.

کلیدواژه‌ها [English]

  • Bayesian Network
  • Drought
  • Monitoring
  • Prediction
  • Support Vector Machine
  • Urmia Lake
  1. Abramopoulos, F., C. Rosenzweig and B. Choudhury. 1988. Improved ground hydrology calculations for global climate models (GCMs): soil water movement and evapotranspiration. Journal of Climate, 1(9): 921-941.
  2. Adamowski, J and S.O. Prasher. 2012. Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data/Porównanie metod uczenia maszynowego do prognozowania spływu w zlewniach górskich na podstawie ograniczonych danych. Journal of Water and Land Development, 17(1): 89-97.
  3. Ahmadi, F., F. Radmanesh, and R. Mirabbasi Najaf Abadi. 2014. Comparison between genetic programming and support vector machine methods for daily river flow forecasting, case study: Barandoozchay River. Journal of Water and Soil, 28(6): 1162-1171. (In Persian)
  4. Ahmadi, F., F. Radmanesh, R. Mir Abbasi Najf Abadi. 2016. Application of bayesian networks and genetic programming for predicting daily river flow, case study: Barandoozchay River. Irrigation Sciences and Engineering, 39(4): 213-223. (In Persian)
  5. Allen, R.G., L.S. Pereira, D. Raes and M. Smith. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements, FAO Irrigation and drainage Paper 56, FAO, Rome, 300(9): D05109.
  6. Behzad, M., K. Asghari, M. Eazi and M. Pallhang. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications, 36: 7624-7629.
  7. Borsuk, M.E., D. Higdon, C.A. Stow and K.H. Reckhow. 2001. A bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones. Ecological Modelling, 143: 165-181.
  8. Botsis, D., P. Latinopoulos and K. Diamantaras. 2011. Rainfall-runoff moeling using suport vector. Regression and Artificial Neural Networks. 12th International Conference on Environmental Science and Technology (CEST2011), Rhodes, Greece, 8-10 September.
  9. Brandt, G and H. Henriksen. 2003. Protection of drinking water sources for quality and quantity, groundwater protection in the Greater Copenhagen area. In: Future Scenarios for Water Management in Europe. FIRMA Conference, 19-20 February, Barcelona, SP.
  10. Da Silva, V.D.P.R. 2004. On climate variability in northeast of Brazil. Journal of Arid Environments, 58, 575-596.
  11. Dibike, Y.B., S. Velickov, D. Solomatine and M.B. Abbott. Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering 15(3): 208–216
  12. Dorner, S., J. Shi and D. Swayne. 2007. Multi-objective modelling and decision support using a bayesian network approximation to a non-point source pollution model. Environmental Modelling and Software, 22: 211-222.
  13. Hamel, L.H. 2011. Knowledge discovery with support vector machines. Vol. 3, John Wiley and Sons.
  14. Hosking, J.R. 2009. L- Wiley StatsRef: Statistics Reference Online.
  15. Kempes, C., O. Myers, D. Breshears and J. Ebersole. 2008. Comparing response of Pinus edulis tree-ring growth to five alternate moisture indices using historic meteorological data. Journal of Arid Environments, 72(4): 350-357.
  16. Labudova, L., L. Schefczyk and G. Heinemann. 2014. The comparison of the SPI and the SPEI using COSMO model data in two selected Slovakian river basins. Paper presented at the EGU General Assembly Conference Abstracts.
  17. Madadgar, S. and H. Moradkhani. 2014. Spatio-temporal drought forecasting within bayesian networks. Journal of Hydrology, 512: 134-146.
  18. Miller, J.F and P. Thomson. 2000. Cartesian genetic programming. Paper presented at the European Conference on Genetic Programming. Climatology, 35(13): 4027-4040.
  19. Mostafazadeh, R., M. Shahabi and M. Zabihi. 2015. Analysis of meteorological drought using Triple Diagram Model in the Kurdistan Province, Iran. Geographical Planning of Space Quarterly Journal, 17: 129-140 (in Persian).
  20. Neapolitan, R.E. 2003. Learning bayesian networks. Prentice Hall Series in Artificial Intelligence, 693 pages.
  21. Nikbakht Shabazi, A.R. 2008. Application of SVM in predicting the river flow. In: Proceedings of 8th Iranian Hydraulic Conference, 24-26 Nov, Tehran University, Tehran, Iran (in Persian).
  22. Nikoo, M.R and R. Karachian. 2009. Bayesian network performance assessment on river water quality management: application of the ratio–trade systems. Journal of Water and Wastewater, 20(1): 23-33.(In Persian)
  23. Potop, V., and M. Možný. 2011. The application a new drought index–standardized precipitation evapotranspiration index in the Czech Republic. Mikroklima a Mezoklima Krajinných Structur a Antropogenních Prostředí, 2: 2-14.
  24. Raziei, T., P. Daneshkar Arasteh, R. Akhtari and B. Saghafian. 2007. Investigation of meteorological droughts in the Sistan and Balouchestan Province, using the standardized precipitation index and Markov chain model. Iran-Water Resources Research, 3(1): 25-35 (in Persian).
  25. Reggiani, P. and A.H. Weerts. 2008. A bayesian approach to decision-marking under uncertainty: an application to real-time forecasting in the River Rhine. Journal of Hydrology, 356: 56-69
  26. Samadianfard, S. and E. 2017. Prediction of SPI drought index using support vector and multiple linear regressions. Journal of Soil and Water Resources Conservation, 6(4): 1-16 (in Persian).
  27. Stagge, J.H., L.M. Tallaksen, L. Gudmundsson, A.F. Van Loon and K. Stahl. 2015. Candidate distributions for climatological drought indices (SPI and SPEI). International Journal of Climatology, 35(13): 4027-4040.
  28. Thornthwaite, C.W. 1948. An approach toward a rational classification of climate. Geographical Review, 38(1): 55-94.
  29. Vapnik, V.N. 1998. Statistical learning theory. Wiley, New York.
  30. Vicente-Serrano, S.M., S. Beguería and J.I. López-Moreno. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23(7): 1696-1718.
  31. Vicente-Serrano, S.M., J.I. López-Moreno, A. Drumond, L. Gimeno, R. Nieto, E. Morán-Tejeda and J. Zabalza. 2011. Effects of warming processes on droughts and water resources in the NW Iberian Peninsula (1930−2006). Climate Research, 48(2/3): 203-212.
  32. Whipple, W. 1966. Regional drought frequency analysis. Journal of the Irrigation and Drainage Division, 92, 11-32.
  33. Yu, P.S., S.T. Chen and I.F. Chang. 2006. Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328: 704-716.
  34. Zareabyaneh, H., M. GHobaeisoogh and A. Mosaedi. 2016. Drought monitoring based on Standardized Precipitation Evaoptranspiration Index (SPEI) under the effect of climate change. Water and Soil (Agricultural Sciences and Technology), 29(2): 374-392 (in Persian).