توسعه مدل ارزیابی آسیب خشکسالی کشاورزی برای گندم دیم در استان کرمانشاه با استفاده از روش‌های آماری و هوشمند

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری

2 دانشیار دانشگاه تربیت مدرس

3 استادیار دانشکده نقشه برداری دانشگاه صنعتی خواجه نصیرالدین طوسی

4 استادیار دانشگاه تربیت مدرس

چکیده

در سال‌های اخیر پدیده خشکسالی خسارت‌های فراوانی به بخش کشاورزی کشور وارد آورده که وجود یک سیستم پیش آگاهی از تأثیر آنبر محصولات کشاورزی را برای کمک به سیاستگذاران و ذی­نفعان ضروری­می­سازد.  در این تحقیق، مدلی برای ارزیابی پیش­بینی آسیب ناشی از خشکسالی کشاورزی برای استان کرمانشاه با استفاده از روش‌های آماری و هوشمند توسعه یافته است.  این مدل به­طور خاص برای محصول گندم دیم است و می­تواند خود را همراه با رشد گیاه و در مراحل مختلف فنولوژیک بهنگام کند.  در فرایند توسعة مدل، از شاخص‌های خشکسالی PDSI، Z-index، CMI، SPI و EDI استفاده و جهت انتخاب متغیرهای مناسب، روش­های الگوریتم ژنتیک، و شبکة مصنوعی عصبی به­کار گرفته شد.  نتایج نشان می­دهد که شاخص Z-index نسبت به بقیه شاخص‌ها آسیب ممکن را بهتر پیش­بینی می‌کند.  همچنین، مدل با گذشت زمان در مراحل مختلف بحرانی رشد از برازش بهتری برخوردار می­شود و به­خصوص از مرحلة سوم به بعد، سطح معنی­داری روابط به 1 درصد رسیده و نتایج پیش­بینی قابل اتکا می­شود.  همچنین، اتصال مدل به محیط سامانه اطلاعات جغرافیایی قابلیت­های آن را برای تحلیل­های لازم مکانی و ارائة کارآمدتر نتایج ارتقاء نمود. 

عنوان مقاله [English]

Agricultural Drought Risk Assessment Model for Kermanshah Province, Using Statistical and Intelligent Methods

چکیده [English]

The agriculture sector has been affected by severe drought in recent years, making development of a drought warning system for agriculture crucial. Such a system can be a useful tool for policy makers and investors. This research develops a model for agricultural drought risk assessment using statistical and intelligent methods. Kermanshah province, a major rain-fed region of Iran, was selected as the study area. The model is specific to rain-fed wheat and was updated during the different phonological stages of the growing season. The inputs are a combination of the PDSI, Z-index, CMI, SPI and EDI drought indices which were selected using genetic algorithm and artificial neural networking techniques. The results show that the Z-index better predicts possible losses. The general performance of the model increased toward the end of the growing season, especially after the third stage, when the significance level of the relation reaches 1% and the results become more reliable. Furthermore, linkage of the model to the geographical information system makes it more capable of spatial analysis and more suitable for presentation of the final results.

کلیدواژه‌ها [English]

  • Agricultural drought
  • Genetic algorithm
  • Kermanshah Province
  • Principle Component Analysis
  • Vulnerability Assessment
Allen, R. G., Pereira, L. S., Raes, D. and Smith, M., 2006. Crop Evapotranspiration. Report prepared for FAO. Water Resour. Develop. Manage. Ser. FAO. Rome. Italy.
Alley, W. M. 1984. The palmer drought severity index: Limitation and assumptions. J. Climate Appl. Meteorol. 23, 1100-1109.
Babb, J. C., Khandekar, M. L. and Garnett, E. R. 1997. An analysis of PNA indices for forcasting summer weather over Canadian prairies with implications for wheat yield and protein. In: Proceeding of the Longe-Range Weather and Crop Forcasting Work Group Meeting III. Canadian Meteorological Center. Dorval.
Bhalme, H. N. and Mooley, D. A.1980. Large-scale droughts/floods and monsoon circulation. Mon. Wea. Rev. 108, 1197-1211.
Boken, V. K. 2005. Monitoring and predicting agricultural drought. Oxford University Press.
Byun, H.R. and Wilhite, D.A. 1999. Objective quantification of drought severity and duration. J. Climate. 117(6): 935-943.
Edwards, D. C. and Mckee, T. B. 1997. Characteristics of 20th century drought in the United States at  multiple time scales. Climatology Report. No. 97-2. Colorado State University. Fort Collins. Colorado.
Hayes, M. J. 2000. What is drought? National Drought Mitigation Center. URL://www.drought. unl.edu/whatis/indices.htm.
Hayes, M. J., Svoboda, M. D., Wihite, D. A. and Vanyarkho, O. V. 1999. Monitoring the 1996 drought using the standardized precipitation index. Bull. Am. Meteor. Soc. 80, 429-438.
Heim Jr, R. R. 2002. A review of 20th drought indices used in the United States. Bull. Am. Meteor. Soc. 83, 1149-1165.
Janikow, C. Z. 1993. A knowledge-intensive genetic algorithm for supervised learning. Machine Learning. 13, 189-228.
Kendall, M. G. and Stuart, A. 1977. The advanced theory of statistics. Charles Griffin and Company: London, High Wycombe.
Kumar, V. 1998. An early warning system for agricultural drought in an arid region using limited data. J. Arid. Environ. 40, 199-209.
Kumar, V. and Panu, U. 1997. Predictive assessment of severity of agricultural droughts based on agro-climatic factors. J. Am. Water Resour. Association. 33(6): 1255-1264.
Legates, D. R. and McCabe, G. J. 1999. Evaluating the use of "Goodness-of-fit" Measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233-241.
McKee, T. B., Doesken, N. J. and Kleist J. 1993. The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology Anaheim. CA. Am. Meteorol. Soc. Boston.
Meyer, S. J., Hubbard, K. G. and Wilhite, D. A. 1993. A crop-specific drought index for corn: I. model development and validation. Agron. J. 86, 388-395.
Moghaddasi, M. 2002. Daily drought monitoring and assessment in Tehran province. M. Sc. Thesis. Tarbiat Modares University.  Tehran. (in Farsi)
Palmer, W. C. 1965. Meteorological drought. Research paper No. 45. US Weather Bureau. Washington DC.
Palmer, W. C. 1968. Keeping track of crop moisture conditions, Nationwide: the Crop Moisture  Index. Weatherwise No. 21.
Pham, D. T. and Karaboga, D. 1998. Intelligent Optimization Techniques. Pub. Springer.
Quiring,S. M. and Papakryiakou, T. N. 2003. An evaluation of agricultural drought indices for the Canadian prairies.  Agric. for. Meteorol. 1, 46-62.
Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., Angel, J. and Stephens, S. 2002. The drought monitor. Bull. Am. Meteor. Soc. 83, 1181-1190.
Thompson, L. M. 1988. Effects of changes in climate and weather variability on the yields of corn and soybeans.  J. Produc. Agric. 1, 20-27.
Thornwaite, C. W. 1948. An approach toward a rational classification of climate. Geogr. Rev. 38.
55-94.
van Rooy, M. P. 1965. A rainfall anomaly index independent of time and space. Notos 14, 43-48.
Walker, G. K. 1989. Model for operational forecasting of western Canada wheat yield.  Agric. For. Meteorol. 44, 339-351.
Wells, N. 2003. PDSI Users Manual: Version 2.0. National Agricultural Decision Support System, University of Nebraska-Lincoln.
Wilhite, D. A. and Neild, R. E. 1982. Determining drought frequency and intensity on the basis of plant response: Wild hay in the sand hills of Nebraska. USA. Agric. Meteorol. 25, 257-265.
Wilhite, D. A. and Glantz, M. H. 1985. Understanding the drought phenomenon: the role of definitions. Water International. 10(3): 111-120.
Wu, H. and Wilhite, D. A. 2004. An operational agricultural drought risk assessment model for Nebraska. USA. Natural Hazards. 33, 1-21.
Zhang, J. 2004. Risk assessment of drought disaster in the maize-growing region of Songliao Plain, Chaina. Agric. Ecosyst. Environ. 102, 133-153.