نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب دانشکده کشاورزی دانشگاه بیرجند ایران

2 استادیار گروه علوم و مهندسی آب دانشکده کشاورزی دانشگاه بیرجند

3 دانشگاه بیرجند مدیر گروه پژوهشی زعفران

چکیده

با توجه به حساسیت عملکرد زعفران و تاثیرپذیری آن از پارامترهای اقلیمی و خاصیت غیرخطی توابع عملکرد گیاهی، در این تحقیق به پیش‌بینی عملکرد زعفران پرداخته شد. هدف از انجام این مطالعه، توانایی مدل شبیه‌سازی ماشین بردار پشتیبان(lssvm) و مدل برنامه‌ریزی بیان ژن(GenXproTools5,0 )در پیش‌بینی عملکرد زعفران براساس داده‌های هواشناسی(حداقل دما، حداکثر دما، بارش، تبخیر و رطوبت نسبی،عملکرد یکسال قبل) در مقیاس فصلی در بازه زمانی 2006-1992 می‌باشد. بهترین مدل براساس معیارهای ارزیابی RMSE,R2وMAE انتخاب شد. بررسی‌ها نشان داد در هر دو مدل، در سناریوی H (میانگین حداقل دما درفصل زمستان، میانگین بارش در فصل پاییز، میانگین بارش در فصل زمستان، میانگین تبخیر در فصل زمستان،عملکرد یکسال قبل) برآورد مطلوب‌تری از عملکرد زعفران حاصل شد. در مدل LSSVM ترکیب‌های با تابع کرنل Liner نتایج دقیق‌تری داشت. اما بین مدل lssvm و مدل GEP، مدل GEP دارای R2 بالاتر و RMSE و MAE پایین‌تری بود. میزان RMSE,R2 و MAE در این مدل تحت سناریوی H در بخش آموزش به ترتیب 0.60688، 0.43265و 0.46432 به دست آمد. در مجموع مدل GEP نتایج دقیق‌تری را در تخمین عملکرد زعفران نسبت به مدل LSSVM دارا بود.

کلیدواژه‌ها

Abarashi, M., Moftah Holghi, H., Sani Khani, A., and Dehghani, A., 2014. Comparison of performance of three intelligent methods in order to predict static level fluctuations (Case study: Zarin Gol plain). Water and Soil Conservation Research. 21(1), 180-163.
Adams, R.M., Aka, C., Mc Carl, B.A., and Schimmelpfennig, D., 2001. Climate variability and climate change: Implications for agriculture. Advances in the Economics of Environmental Resources. 3, 115-148.
Aghamohammadi, S., Khashei-Siuki, A., Shahidi, A., and Hashemi, R., 2017. Evaluation of artificial neural network model in predicting saffron yield using standardized drought index in South Khorasan and Khorasan Razavi provinces. The First National Conference on New Opportunities for Agricultural Production and Employment in the East of the Country (in Line with the Objectives of the Resistance Economy). [in Persian].
Akbarpour, A., Khorashadizadeh, O., Shahidi, A., and Ghochanian, E., 2013. Performance evaluation of artificial neural network models in estimate production of yield saffron based on climate parameters. J. Saffron Res. 1(1), 27-35. [in Persian with English Summary].
Akrami, M.R., Malakouti, M.J., and Keshavarz, P., 2014. Study of flower and stigma yield of saffron as affected by potassium and zinc fertilizers in Khorasan Razavi Province. J. Saffron Res. 2(1), 85-96. [in Persian with English Summary].
Alkroosh, I., and Nikraz, H., 2013. Evaluation of pile lateral capacity in clay applying evolutionary approach. International Journal of GEOMATE, 14(1 SERL 7), 462-466.‏
Azamathulla, H.M., 2012. Gene expression programming for prediction of scour depth downstream of sills. J. Hydrol. 460, 156-159.
 Behdani, M.A., Koocheki, A.R., Nassiri Mahallati, M., and Rezvani Moghaddam, P., 2005. Evaluating the relationships between revenue and consumption of nutrients in Crocus sativus. Iran. J. Field Crops Res. 3(1), 1-14. [in Persian with English Summary].
Borelli, A., De Falco, I., Della, C.A., Nicodemi, M., and Trautteur, G., 2006. Performance of genetic programming to extract the trend in noisy data series. Physica A. 370, 104-108.
Ghobadian, M., Ghorbani, A., and Khalaj, M., 2013. Investigating the gene expression function in Zangmar. River Flood Journals Compared to Dynamic Wave. Water and Soil Journal (Agricultural Science & Technology) 27(3), 592-602. [in Persian with English Summary].
Kavusi, M., Khashei-Siuki, A., Pourreza Bildeni, M., and Najafi Mod, M., 2017. Evaluation of the least squares of the vector carriers model in estimating evaporation and comparing it with experimental models. Journal of Environmental Water Engineering. 3, 235-247.
Kisi, O., Shiri, J., and Tombul, M., 2013. Modeling rainfall-runoff process using soft computing techniques. .Computers and Geosciences 51, 108–117.
Koocheki, A., Karbasi, A.R., and Seyyedi, S.M., 2017. Some reasons for saffron yield loss over the last 30 years period. Saffron Agron. & Technol. 5(2), 107-122. [in Persian with English Summary].
Koozehgaran, S., Mousavi Baygi, M., Sanaeinejad, S.H., and Behdani, M.A., 2011. Study of the minimum, average and maximum temperature in South Khorasan to identify relevant areas for saffron cultivation using GIS. Journal of Water and Soil. 25(4), 892-904. [in Persian with English Summary].
Muzzammil, M., Alam, J., and Danish, M., 2015. Application of gene expression programming in flood frequency analysis. Journal of Indian water Resources Society, 35(2), 1-6.75.
Nekouei, N., Behdani, M.A., and Khashei-Siuki, A., 2014. Predicting saffron yield from meteorological dataUsing expert system, Razavi and South Khorasan Province, Iran. J. Saffron Res. 2(1), 1-19. [in Persian with English Summary].
Norouzi, M., Ayoubi, S., Jalalian, A., Khademi, Abbaspour, K.C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., and Srinivasan, R., 2007. Modelling hydrology and water quality inthe pre-alpine/alpine Thur watershed using SWAT. J. Hydrol. 333(2), 413-430. [in Persian with English Summary].
Rezai, A., Khashei-Siuki, A., and Shahidi, A., 2015. Ground water level monitoring network design using the least squares support vector machine (LS-SVM). Iran Soil and Water Research. 45(4), 389-396. [in Persian with English Summary].
Rezai, A., Shahidi, A., Khashei-Siuki, A., and Riahi Madvar, H., 2014. Performance evaluation least squares support vector machine model to predict the water table. Journal of Irrigation and Drainage. 7(4), 510-520. [in Persian with English Summary].
Riahi Modavar, H., Khashei-Siuki, A., and Seifi, A., 2017. Accuracy and uncertainty analysis of artificial neural network in predicting saffron yield in the south Khorasan province based on meteorological data. Saffron Agron. & Technol. 5(3), 255-271. [in Persian with English Summary].
Sadeghi, B., 1993. Effect of corm weight on saffron flower collection. Publication of Scientific Research- Technology Research Center of Khorasan, Iran. [in Persian].
Sanchez, A.S., Nieto, P.J.G., Fernandez, P.R., Diaz, J.J.D., and Iglesias-Rodr, F.J., 2011. Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Mathematical and Computer Modelling. 54, 1453–1466.
Seifi, A., Myrlotfy, S., and Riahi, H.,2012. Assessment and monitoring of weather station network using principal components analysis and factor analysis Case study: Kerman. Journal of Irrigation and Drainage, 5(1), 3-0-42.  [in Persian with English Summary].
Seydou, T., and Aytac, G., 2012. Regional-Specific numerical models of evapotranspiration using Gene-Expression Programming interface in Sahel. Water Resources Management. 26, 4367-4380.
Traore, S., Luo, Y., and Fipps, G., 2017. Gene-Expression Programming for Short-Term Forecasting of daily reference evapotranspiration using public weather forecast information. J. Water Resources Management.