Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water

Abstract

Wheat, barley, sugerbeet, potato, alfalfa, and corn are common crops produced in Iran, which need the most virtual water volume compared to other crops. Determination of the virtual water for these crops would assist in better management of water resources. The main objective of this study is to find out the best technique for estimating and mapping of virtual water. In this research, the virtual water volume was determined by crop water requirement and crop yields using three ANN structures as well as ANFIS technique. Based on RMSE and R 2 the comparison of obtained results predicted through the applied ANNs structures indicate that the RBF outperforms the other models for estimating virtual water for wheat, potato, corn, and barley. Moreover, a comparison between RBF and ANFIS revealed that ANFIS is a promising model, which can be efficient mathematical tool for estimation of crop"s virtual water.

Optimization of the Cropping Pattern of Main Cereals and Improving Water Productivity: Application of the Water Footprint Concept

“…With an area about 1.6 million km 2 , it is located between 25°03′ and 39°47′ N and 44°5′ and 63°18′ E (Figure 1). Its altitudes vary from 40 m-MSL (mean sea level) to 5678 m + MSL (Ahmadaali et al, 2013). About 70% of its area has low precipitation (< 250 mm yr À1 ) and only 3% of its area has an average precipitation above 500 mm yr À1 (Montazar and Zadbagher, 2010;Mesgaran et al, 2017).…”

Prediction of wind drift and evaporation losses of a sprinkler irrigation system using principal component analysis and artificial neural network technique

“…Thus, modelling WDEL stands as an adequate approach. The artificial neural network (ANN) technique has proved capable of successfully addressing problems that differ widely in nature (Hota, 2014;Arif et al, 2012;Hardaha et al, 2012;Karasekreter et al, 2012;Ahmadaali et al, 2013;Nithya and Srinivasan, 2015;Da Rocha Neto et al, 2015). This is due to its ability to describe complex real-world problems, especially when the relationships between the dependent and independent variables are unclear.…”