Current World Environment Vol. 11(2), 637-647 (2016) Application of Artificial Neural Network Approach for Estimating Reference Evapotranspiration KHYATI N. VYAS 1 and R. SUBBAIAH 2 1 Soil and Water Engineering Department, College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, Gujarat, India, 2 Soil and Water Engineering Department, College of Agricultural Engineering and Technology, Junagadh Agricultural University, Junagadh, Gujarat, India. *Corresponding author Email: khyativyas46@gmail.com http://dx.doi.org/10.12944/CWE.11.2.36 (Received: May 21, 2016; Accepted: August 22, 2016) ABSTRACT The process of evapotranspiration (ET) is a vital part of the water cycle. Exact estimation of the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is essential in agriculture, its over-estimation leads to cause the waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. The well known Penman-Monteith (PM) equation always performs the highest accuracy results of estimating reference Evapotranspiration (ET 0 ) among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country. ET 0 is a complex process which is depending on a number of interacting meteorological factors, such as temperature, humidity, wind speed, and radiation. The lack of physical understanding of ET 0 process and unavailability of all appropriate data results in imprecise estimation of ET 0 . Over the past two decades, artificial neural networks (ANNs) have been increasingly applied in modeling of hydrological processes because of their ability in mapping the input–output relationship without any understanding of physical process. This paper investigates for the first time in the semiarid environment of Junagadh, the potential of an artificial neural network (ANN) for estimating ET 0 with limited climatic data set. Keywords: Artificial neural network, Evapotranspiration, Reference evapotranspiration, Feed forward back-propagation, Penman Monteith equation. INTRODUCTION In semi arid regions, water resources management is a crucial requirement for increasing agricultural production because food insecurity is becoming a main concern. ET is one of the hydrologic cycle components and the precise estimation of ET is very important for the researches such as water balance, irrigation design and management, crop yield modelling, and water resources planning and management reported by Kumar et al. 5 (2002). ET 0 can be obtained by many estimation methods, but Shih et al. 6 (1983) reported that the factors such as data availability must be considered when choosing the ET 0 calculation technique. The Penman-Monteith method is maintained as the single standard method recommended by the FAO for the computation of ET 0 from complete meteorological data [Allen et al. 2 (2006); Smith et al. 7 (1990)] but, the main shortcoming of the FAO 56 PM method is, it requires