مقایسه توانایی پیش‌بینی نوسان‌های جمعیت سن گندم توسط مدل‌های سیستم استنتاج عصبی- فازی تطبیقی (ANFIS)، روش سطح پاسخ (RSM) و رگرسیون خطی چند متغیره

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه گیاه‌پزشکی، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه، ایران

2 گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه، ایران

چکیده

سن معمولی گندم یکی از آفات اصلی گندم و از مهم‌ترین مسائل گیاه­پزشکی ایران است. از دیرباز مدل‌های رگرسیون خطی چندگانه برای پیش­بینی نوسان­های جمعیت آفات مختلف با استفاده از متغیرهای محیطی مورد استفاده قرار گرفته‌اند. استفاده از سیستم‌های هوشمند برای تخمین دقیق‌تر نوسان­های جمعیت حشرات می‌تواند نتایج بهتری را به همراه داشته باشد. بنابراین مطالعه‌ای با هدف پیش­بینی نوسان­های جمعیت سن گندم با استفاده از سیستم استنتاج فازی عصبی- تطبیقی، روش سطح پاسخ و رگرسیون خطی چند متغیره انجام شد. این پژوهش طی سال‌های 1394و 1395 در دو مزرعه گندم آبی یک هکتاری در شهرستان چادگان انجام شد. در این مدل‌ها، میانگین دما، میانگین رطوبت نسبی، بارش، سرعت و جهت باد، روز نمونه برداری، روز- درجه و ارتفاع از سطح دریا به عنوان متغیرهای پاسخ استفاده شدند. داده‌های جمع­آوری شده به صورت تصادفی به دو دسته آموزش (70 درصد) و آزمون (30 درصد) تقسیم شدند و از آن‌ها برای آموزش و ارزیابی مدل‌های انفیس، روش سطح پاسخ و همچنین رگرسیون خطی استفاده شد. دقت پیش­بینی به وسیله آماره‌های R2و RMSE ارزیابی شد. نتایج، کارایی بالاتر مدل انفیس )0614/0, RMSE= 93/0= (R2و روش سطح پاسخ )0836/0, RMSE= 88/0= (R2را نسبت به مدل رگرسیون خطی چند متغیره )23/0, RMSE= 34/0= (R2نشان داد. همچنین تحلیل حساسیت حاکی از آن بود که میانگین دما، رطوبت نسبی، سرعت باد و روز نمونه­برداری پارامترهای موثر بر پیش­بینی تراکم سن مادر بودند.

کلیدواژه‌ها


عنوان مقاله [English]

Comparing the ability of ANFIS, RSM and multiple linear regression models for estimation of Eurygaster integriceps population

نویسندگان [English]

  • Z. Dustiy 1
  • N. Moeini-Naghadeh 1
  • A. A. Zamani 1
  • L. Naderloo 2
1 Department of Plant Protection, College of Agricultural Science, Razi University, Kermanshah, Iran
2 Department of Biosystem Mechanization Engineering, College of Agricultural Sciences, Razi University, Kermanshah, Iran
چکیده [English]

The Sunn pest, Eurygaster integriceps Put. is one of the main pests of wheat and one of the most important plant protection problems in Iran. Multiple linear regression models have been used to predict the fluctuation of various pest populations by using environmental variables. Using intelligent systems to better estimate insect population fluctuations can lead to better results. Therefore, the current study was conducted to predict population fluctuation of the Sunn pest by using a neuro adaptive fuzzy inference system, a response surface method and multiple linear regression. This study was done during in 2015 and 2016 on two wheat farms each one with an area of one hectare in the Chadegan county. In these models, average temperature, average relative humidity, rainfall, wind speed and direction, sampling date, degree- day and altitude from sea level were used as response variables. The collected data randomly divided in two categories of training (70%) and testing (30%) and they used for train and test of ANFIS and response surface methodology. The accuracy of the prediction was evaluated by R2 and RMSE. The higher performance of the ANFIS model (R2= 0.93, RMSE= 0.0614) and RSM (R2 = 0.88, RMSE= 0.0836) resulted comparing to the multiple linear regression (R2 = 0.23, RMSE= 0.34). Also the results of sensitivity analysis indicated that the average of temperature, relative humidity, wind speed and date of sampling were the most important parameters for predicting density of adult Sunn pest. 

کلیدواژه‌ها [English]

  • Predictable models
  • Eurygaster integriceps
  • Anfis
  • Environmental factors
  • Chadegan
Arkhipov, M. E., Crueger, M. and Kurtener, D. 2008.Evaluation of ecological conditions using bioindicators: application of fuzzy modeling. Lecture Notes in Computer Science 5072: 491–500.

Azizian, M. S. and Moradi, B. 2012.Study and analyzing the effects of rainfall and drought conditions on the outbreak of Sunn pest in Sanandaj city. The first national agricultural conference in difficult environments. Ramhormaz pp. 10. https://www.civilica.com/Paper-NCAHEC01-NCAHEC01_251.html. (In Farsi)

Balan, B., Mohaghegh, S. and Ameri, S. 1995. State- of- Art- in permeability determination from well log data:Part 1- A comparative study, Model development. Society of Petroleum Enginners 30978: 17-25.

Bianconi, A., Von Zuben, C. J., Serapião, A. B. S. and Govone, J. 2010.Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala. Journal of Insect Science 10: 1-18.

Brown, E. S. and Eralp, M. 1962.The distribution of the species of Eurygaster integriceps in Middle East countries. The Journal of Natural History 5: 63-77.

Buragohain, M. and Mahanta, C. 2008.A novel approach for ANFIS modeling based on full factorial design, Applied Soft Computing 8: 609–625.

Cheng, C. B., Cheng, C. J. and Lee, E. S. 2002.Neuro-fuzzy and genetic algorithm in multiple response optimization, Computers and Mathematics with Applications 44: 1503–1514.

Erahaghi, I., Xuchai, L., Mahnaz, H. and Yusuf, S. 1993.A robust neural network model for pattern recognition of pressure transient test data. Society petroleum engineering annual technical conference and exhibition, 3–6 October 1993. Houston, Texas.

Gorgipour Aftahi, M., Sadeghi, A., Nazemi Rafi, G. and Ghobari, H. 2014.Study of the relationship between density Sunn pest (Eurygaster integriceps Put) in field with moisture (rainfall) after complete loss of wintering places. The First national conference on e-agriculture and sustainable natural resources. Tehran. Arvand Mehr institution of higher education. http://www.civilica.com/Paper-NACONF01-NACONF01_1195.html.

Howe, P. D., Bryant, S. R. and Shreeve, T. G. 2007. Predicting body temperature and activity of adult Polyommatusi carususing neural network models under current and projected climate scenarios. Oecologia 153: 857–869.

Jamali, A., Nariman-Zadeh, N., Darvizeh, A., Masoumi, A. and Hamrang, S. 2009. Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Engineering Applications of Artificial Intelligence 22(4-5): 676-687.

Karimzadeh, R., Hejazi, M. J., Helali, H., Iranipour, Sh. and Mohammadi, A. 2012.The relationship between dynamic population Eurygaster integriceps with environmental variables in East Azarbaijan province. Journal of Plant Protection Sciences 43 (1): 165-177 (In Farsi)

Metin, E. H. and Murat, H. 2008.Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system. International Journal of Refrigeration 31: 1426–1436.

Mittal, G. S, and Zhang, J. 2000. Prediction of temperature and moisture content of frankfurters during thermal processing using neural network. Journal of Applied Poultry Research78(7): 13-24.

Moeini Naghadeh, N. 2002.Regional degree- day forecasting model for predicting developmental stages of sunn pest in the field under variable temperature. Ph.D. thesis. Tarbiat Modares University. Tehran 90 pp.

Mozafari, Gh. and Eghbali Babadi, F. 2013.Analysis of temperature and rainfall characteristics on the downward of sunn pest in Isfahan province. Journal of Lecturer in Humanities - Space Planning and Design 17 (3): 28-44. (In Farsi)

Mozaffari, Gh. A. and Azizian, M. S. 2011.A study of outbreak of sunn pest on the basis of temperature characteristics in Kurdistan province (Case study: Bijar city). Journal of Natural Geography Research 76: 121-135. (In Farsi)

Naderloo, L., Alimardani, R., Omid, M. F., Sarmadian, P., Javadikia, H. and Torabi, M. Y. 2012.Application of ANFIS to predict crop yield based on different energy inputs. Journal of Measurement 45: 1406-1413. (In Farsi)

Pedigo, L. P. and Buntin, G. D. 1993.Handbook of sampling methods for arthropods in agriculture. CRC Press, Boca Raton, FL. 714.

Radjabi, Gh. 2000.Ecology of cereals sunn pests in Iran. Agricultural education publication. Tehran. Iran. 343 pp. (In Farsi)

Radjabi, Gh. 2001. Investigation on the downward migration of hibernating Sunn pest individuals from the altitudes to the cereal fields in Varamin region. Journal of Pests and Plant Diseases68(1): 107-122. (In Farsi)

Radjabi, Gh. 2007.Sunn pest management based on its outbreaks' key factor analysis in Iran. Agricultural Education Publications, Tehran, Iran. 163 pp. (In Farsi)

Richard, A. 2004.Regression Analysis: A Constructive Critique. Sage Publications. Thousand Oaks, CA.

Serge, G. 2001.Designing fuzzy inference systems from data: Interpretability oriented review. IEEE Transaction on Fuzzy Systems 9 (3): 426–442.

Sobhani, B., Salahi, B and Goldoost, A. 2014.Study of dust and evaluation of its prediction based on statistical methods and ANFIS model in Zabol station. Journal of Geography and Development 13 (38): 123-138. (In Farsi)

Witek-Krowiak, A., Chojnacka, K., Podstawczyk, D., Dawiec, A. and Pokomeda, K. 2014. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process. Bioresource Technology 160: 150-160.

Worner, S. P. and Gevrey, M. 2006. global insect pest species assemblages to determine risk of invasion. Journal of Applied Ecology 43: 858-867.