Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Agriculture, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, Iran.

2 Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran.

Abstract

In this study, barley yield has been estimated via radial basis function network (RBF) and feed-forward neural networks (GFF) models of artificial neural network (ANNs) in Torbat-Heydarieh of Iran. For this purpose, a dataset consists of 200 data at three levels of irrigation with well water, industrial wastewater (sugar factory wastewater), a combination of well water and wastewater in two levels (complete irrigation and irrigation with 75 % water stress) and soil characteristics of area were used as input parameters. To achieve this goal, based on the number of data and inputs, 200 barley field experiments data set were used, of which 80 % (160 data) was used for the training and 20 % (40 data) for the testing the network. The results showed that RBF model has high potential in estimating barley yield with Levenberg Marquardt training and 4 hidden layers. Also the values of statistical parameters R2 and RMSE were 0.81 and the 33.12, respectively. In general, the results showed that ANNs model is able to better estimate the barley yield when irrigation water level parameter with well water is selected as input.

Keywords

Adab H., Farajzadeh M., Filehkoush A., Esmaieli, R., Estimition of corn yield using artificial neural networks, Geographical Space Journal 41 (2013) 171-180.
Akbarpour A., Khorashadizadeh A., Shahidi A Ghochanian A., Evaluation of artificial neural network model in estimation of Saffron crop performance based on climate parameters, Saffron Research Journal 1 (2013) 27-35.
Akbari A., Zaji A.H., Azimi H., Vafaeifard M., Predicting the discharge coefficient of triangular plan form weirs using radian basis function and M5’ methods, Journal of Applied Research in Water and Wastewater 4 (2017) 281-289.
Alborzi M., Introduction to neural networks, 5 th ed. Sharif University of Technology Scientific Publishing Institute: Iran; (1998).
Aljairry H., 2D-flow analysis through zoned earth dam using finite element approach, Engineering and Technology Journal 28 (2010) 1-10.
Azimi H., Heydari M., Shabanlou S., Numerical simulation of the effects of downstream obstacles on malpasset dam break pattern, Journal of Applied Research in Water and Wastewater 5 (2018) 441-446.
Alvarez A., Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach, European Journal of Agronomy 30 (2009) 70-77.
Bagheri S., Gheysari M., Ayoubi Sh. A., Lavaie N., Preparation of autumn repeseed yield map using perceptron neural network, Journal of Plant Production Research 19 (2012) 77-96.
Bariklo A., Alamdari P., Moravaj K., Servati M., Prediction of irrigated wheat yield by using hybrid algorithm methods of artificial neural networks and genetic algorithm, Journal of Water and Soil 31 (2017) 715-726. Eitzinger, J., Formayer, H., Kubu, G., Schaumberger, A., Comparison of simplified methods for drought yield loss detection on crops and grassland in Austria, Hydrometeorological Service of Republic of Macedonia: Conference on Water Observation and Information Systems for Decision Support (2006) 23-26.
Esmaielzadeh-KordKheili S., Estimition of rice yield using statistical methods, artificial neural network and multi-regression methods in Giullan, M.Sc. Thesis, Vali-Asr University, Rafsanjan (2012).
Jafari M.H., Prediction seepage of dam embankment using data analysis methods, M.Sc. Thesis, Islamic Azad University of Semnan (2014).
Gorbani M.A., Shahabboddin S., Zare Haghi D., Azani A., Bonakdari H., Ebtehaj I., Application of firefly algorithm-based support vector machines for prediction of filed capacity and permanent wilting point, Soil and Tillage Research 172 (2017) 32-38.
Kaul M., Hill R.L., Walthall C., Artificial neural networks for corn and soybean yield prediction, Agricultural Systems 85 (2005) 1-18.
Landeras G., Ortiz-Barredo A., López J.J., Forecasting weekly evapotranspiration with ARIMA and artificial neural network models, Journal of Irrigation and Drainage Engineering 135 (2009) 323-334.
Menhaj M.B, Computational intelligence, The basic of artificial neural networks, 12 th ed. Amirkabir University: Iran; (1998).
Merdun H., Çınar Ö., Meral R., Apan M., Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity, Soil & Tillage Research 90 (2006) 108-116.
Nourani V., and Babakhani A., Integration of artificial neural networks with radial basis function interpolation in earth fill dam seepage modeling, Journal of Computing in Civil Engineering 27 (2013) 185-195.
Norouzi M., Prediction of rainfed wheat yield using artificial neural network in Ardal district of Chaharmahal and Bakhtiari province, M.Sc. Thesis, Collage of Agriculture, Isfahan University of Technology, Isfahan, Iran (2009).
Piri J., Amin S., Moghaddamnia A., Keshavarz A., Han D., Remesan R., Daily pan evaporation modeling in a hot and dry climate, Journal of Hydrologic Engineering 14 (2009) 803-811.
Rahmani A., Liaghat A., Khalili A., Estimation of barely yield in east-Azarbaijan using meteorological parameters and drought indicators by artificial neural network, Iranian Journal of Soil and Water Research 39 (2008) 47-56.
Seiler R.A., and Kogan F., AVHRR-based vegetation and temperature condition indices for drought detection in Argentiana, Advances in Space Research 21 (1998) 481-484.
Smith B.A., Hoogenboom G., McClendon R.W., Artificial neural networks for automated year-round temperature prediction, Computers and Electronics in Agriculture 68 (2009) 52-61.
Taghizadeh Mehrjerdi R., Seyedjalali S.A., Sarmadian F., Prediction of corn spatial yield by soil digital mapping in Gotend region (Khuzestan Province, IRAN), Journal of Plant Production 19 (2016) 70-96.
Tiscareno-Lopez M., Izaurralde C., Rosenberg N.J., Baez-Gonzalez A.D., Salinas-Garcia J., Modeling El nino southern oscillation climate impact on Mexican agriculture, Geofisica Internacional 42 (2003) 331-339.
Zareh-Abianeh H., Evaluation of artificial neural network and land statistics in estimation of spatial distribution of Wheat yields (case study: Khorasn-Razavi), Natural Geography Research 44 (2012) 23-42.