ارزیابی عملکرد روش‌های شبکه‌ عصبی مصنوعی و سیستم استنتاج عصبی-فازی تطبیقی در تخمین تبخیر-تعرق گیاه مرجع در شرایط گلخانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

2 استاد گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج

3 گروه علوم باغبانی ، دانشکده مهندسی و فناوری کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

چکیده

برآورد دقیق تبخیر-تعرق گیاه مرجع (ETo) یکی از عوامل مهم در مدیریت و برنامه‌ریزی آبیاری در بخش کشاورزی است. استفاده از روش استاندارد فائو-پنمن-مانتیت برای تخمین ETo، مشروط به در دسترس بودن پارامترهای اقلیمی شامل دما، رطوبت، تابش، سرعت باد و هم-چنین فراهم بودن فرضیات ذکر شده در نشریه فائو 56 است. در بعضی مناطق یا در کشت‌های کنترل شده گلخانه، اغلب دسترسی به تمام پارامترهای اقلیمی و یا برآوردن فرضیات روش فائو-پنمن-مانتیت امکان‌پذیر نیست. بنابراین، بهره‌گیری از روش‌هایی که با پارامترهای کمتری بتواند تخمین دقیقی از ETo ارائه نماید، ارجح است. پژوهش حاضر با هدف ارزیابی عملکرد دو روش شبکه‌های عصبی مصنوعی (ANNs) و سیستم استنتاج عصبی-فازی تطبیقی (ANFIS) برای برآورد ETo در گلخانه تحقیقاتی واقع در دانشکده کشاورزی و منابع طبیعی دانشگاه تهران انجام شد. بر اساس پارامترهای اقلیمی اندازه‌گیری شده در داخل گلخانه، ترکیب‌های مختلفی ایجاد و شاخص‌های ارزیابی برای هر روش و سناریو محاسبه گردیدند. بهترین ساختار شبکه عصبی برای سناریو 4 (ورودی‌های تابش، دما، رطوبت) با 7 نورون در لایه میانی و الگوریتم آموزش تنظیم بیزی به دست آمد. طراحی شبکه‌های ANFIS با توابع عضویت‌های مختلف انجام شد. نتایج نشان داد، تفاوت قابل ملاحظه‌ای بین دقت مدل‌سازی ETo در روش ANFIS تحت سناریوهای مختلف وجود ندارد. به عبارت دیگر در این روش، حتی با داده‌های دما و رطوبت نیز می‌توان با دقت بالایی ETo در داخل گلخانه را شبیه‌سازی نمود. مقایسه شاخص‌های ارزیابی بین مدل‌های ANFIS و ANNs نشان داد، مدل ANFIS عملکرد بهتری نسبت به روش ANNs دارد. به طوری‌که شاخص جذر میانگین مربعات خطای نسبی (RRMSE)، برای سناریوهای 1 تا 4 در مدل ANFIS به ترتیب برابر با 41/1، 80/0، 06/1 و 01/1 درصد و در مدل ANNs برابر با 70/12، 23/2، 12/2 و 10/2 درصد بود.

کلیدواژه‌ها

عنوان مقاله [English]

Performance Evaluation of Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System in Estimating Reference Evapotranspiration under Greenhouse Conditions

نویسندگان [English]

  • Hadisseh Rahimikhoob 1
  • Teymour Sohrabi 2
  • Mojtaba Delshad 3
1 Irrigation and Reclamation Engineering Department, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 professor of Irrigation and Development Engineering Group, Faculty of Agriculture Engineering & Technology, Agriculture and Natural Resources Campus, University of Tehran,karaj
3 Horticultural Sciences Department, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

One of the important factors for agricultural irrigation management and planning is accurate estimation of reference evapotranspiration (ETo). The application of the standard model (FAO-Penman-Monteith) to estimate ETo is restricted due to the availability of climatic variables including temperature, humidity, radiation, wind speed as well as the availability of the mentioned hypotheses in FAO 56. Accessibility to all climatic parameters or satisfaction of the FAO-Penman-Monteith assumptions are often not possible in some areas or in controlled environments (greenhouses). Therefore, it is preferable to use methods that can provide an accurate estimate of ETo with fewer input parameters. The aim of the present study was to evaluate the performance of two methods of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) for estimating ETo in the research greenhouse of the College of Agriculture and Natural Resources of the University of Tehran, located in Karaj, Iran. Based on the measured climatic parameters inside the greenhouse, different combinations were created and evaluation indicators were calculated for each method and scenario. The best neural network structure was obtained for Scenario 4 (radiation, temperature, humidity) with 7 neurons in the hidden layer and Bayesian Regularization training algorithm. ANFIS model was designed with different membership functions. The results showed that there was no significant difference between the performance of the ANFIS method under different scenarios. In other words, even with temperature and humidity data, ETo can be simulated with high accuracy by ANFIS method. Comparison of evaluation indicators between ANFIS and ANNs models showed that ANFIS performed better than ANNs method. The calculated relative root mean square error (RRMSE) for scenarios 1 to 4 in ANNs model was equal to 12.70, 2.23, 2.12 and 2.10% however it was equal to 1.41, 0.80, 1.06 and 1.01% in ANFIS model.

کلیدواژه‌ها [English]

  • Reference evapotranspiration
  • Artificial neural networks
  • Adaptive neuro-fuzzy inference system

مراجع

Abiodun, O., Jantan, A., Omolara, A., Dada, K., Mohamed, N. and Arshad, H., 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11): 00938.
Abyaneh, H., Nia, A., Varkeshi, M., Marofi, S. and Kisi, O., 2011. Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration. Journal of Irrigation and Drainage Engineering, 137(5): 280-286.
Adnan, R., Mostafa, R., Islam, A., Kisi, O., Kuriqi, A. and Heddam, S., 2021. Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms. Computers and Electronics in Agriculture, 191:106541.
Allawi, M., Jaafar, O., Ehteram, M., Mohamad Hamzah, F. and El-Shafie, A., 2018. Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System. Water Resources Management, 32(10): 3373-3389.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M., 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome.
Antonopoulos, V. and Antonopoulos, A., 2017. Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Computers and Electronics in Agriculture, 132: 86-96.
Bhat, N. and McAvoy, T., 1990. Use of neural nets for dynamic modeling and control of chemical process systems. Computers & Chemical Engineering, 14(4-5): 573-582.
Cobaner, M., 2011. Evapotranspiration estimation by two different neuro-fuzzy inference systems. J. Hydrol. 398 (3–4), 292–302.
Costa, L., Kunwar, S., Ampatzidis, Y. et al., 2021. Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning. Precision Agric. https://doi.org/10.1007/s11119-021-09864-1
Dou, X. and Yang, Y., 2018. Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems. Computers and Electronics in Agriculture, 148: 95-106.
Gonzalez del Cerro, R., Subathra, M., Manoj Kumar, N., Verrastro, S. and Thomas George, S., 2021. Modelling the daily reference evapotranspiration in semi-arid region of South India: A case study comparing ANFIS and empirical models. Information Processing in Agriculture, 8(1): 173-184.
Hargreaves, G.H, Samani Z.A, 1985 Reference crop evapotranspiration from temperature. Appl. Eng. Agric., 1(2):96–99.
He, Z. 2014. Artificial neural network model of forecasting relative humidity in different humid and arid areas of China. Computer Modelling and New Technologies.18(6): 225-232
Irmak, S., Irmak, A., Allen, R.G., Jones, J.W. 2003. Solar and net radiation-based equations to estimate reference evapotranspiration in humid climates. Journal of Irrigation and Drainage Engineering. 129: 336–347.
Jang, J., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybernet. 23 (3): 665–685.
Jain, S.K., Nayak, P.C., Sudheer, K.P., 2008. Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrol. Process. 22 (13): 2225–2234.
Kaur, S., Randhawa, S. and Malhi, A., 2021. An efficient ANFIS based pre-harvest ripeness estimation technique for fruits. Multimedia Tools and Applications, 80(13): 19459-19489.
Kouadio, L., Deo, R., Byrareddy, V., Adamowski, J., Mushtaq, S. and Phuong Nguyen, V., 2018. Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Computers and Electronics in Agriculture, 155: 324-338.
Kumar, M., Raghuwanshi, N.S., Singh, R., 2011. Artifical neural networks approach in evapotranspiration modelling: a review. Irrigation Science. 29 (1): 11–25.
Lohani, A., Kumar, R. and Singh, R., 2012. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology, 442-443.
McCulloch, W. and Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4): 115-133.
Mamdani, E., 1975. Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies, 8(6): 669-678.
Monteith, J.L., Unsworth, M.H. 1990. Principles of Environmental Physics (Second ed.). Edward Arnold.
Movahednejad M.H., Saedi S.I. 2020. Development of an ANN Model for the Prediction of Plant Actual Evapotranspiration under a Hydroponic Growing System. Iranian Journal of Irrigation and Drainage.4(14): 1164-1174.
Partel, V., Charan Kakarla, S. and Ampatzidis, Y., 2019. Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture, 157: 339-350.
Priestley, C., and Taylor R. J., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100: 81–92
Rahimikhoob, H., Sohrabi, T. and Delshad, M. ,2020. Assessment of reference evapotranspiration estimation methods in controlled greenhouse conditions. Irrigation Science .38: 389–400 https://doi.org/10.1007/s00271-020-00680-5
Rahimikhoob, H., Sohrabi, T. and Delshad, M., 2021. Sensitivity Analysis of Reference Crop Evapotranspiration Estimation Methods to Meteorological Factors in Greenhouse Conditions. Iranian Journal of Irrigation and Drainage.15(2): 307-315.
Sayed, T., Tavakolie, A., Razavi, A., 2003. Comparison of adaptive network based fuzzy inference systems and B-spline neuro-fuzzy mode choice models. Water Resour. Res. 17 (2): 123–130.
Shiri, J., Nazemi, A., Sadraddini, A., Landeras, G., Kisi, O., Fakheri Fard, A. and Marti, P., 2014. Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. 108: 230-241.
Sutton, R. and Barto, A., 1998. Reinforcement Learning: An Introduction (Adaptive computation and machine learning). MIT Press.
Tabari, H., Kisi, O., Ezani, A. and Hosseinzadeh Talaee, P., 2012. SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology, 444-445: 78-89.
Takagi, T., Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybernetics SMC-15 (1): 116–132.
Wang, L., Kisi, O., Zounemat-Kermani, M., Li, H., 2017. Pan evaporation modeling using six different heuristic computing methods in different climates of China. J. Hydrol. (Amst) 544: 407–427.
Yamaç, S. and Todorovic, M., 2020. Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agricultural Water Management, 228: 105875.
Zeleke, K. and Wade, L. 2012. Evapotranspiration Estimation using Soil Water Balance, Weather and Crop Data. In A. Irmak (Ed.), Evapotranspiration. Remote Sensing and Modeling (1 ed). 41-58.
 
نشریه آبیاری و زهکشی ایران
دوره 16، شماره 3 - شماره پیاپی 93
مرداد و شهریور 1401
صفحه 499-511

فایل ها

هم رسانی

ارجاع به این مقاله

آمار