## 1 Introduction  
Evapotranspirationiration is a key element in water balance as well as in the surface energy equation. Accurate estimation of ETo provides useful information for water resource management, irrigation scheduling, and crop sustainability. Lysimeters are commonly used to directly estimate ETo; however, the use of lysimeters for ETo estimation is very limited because of high maintenance and operational costs on lysimeters [1]. Several mathematical models indirectly estimate ETo and are considered to be the intelligent alternative of direct methods due to time saving and ease of application [2]. Penman-Monteith (FAO-56) is the most acceptable mathematical model for estimation of ETo [3]. Under both humid and arid climatic conditions, the FAO-56 method has been unanimously reported to be the most efficient method for ETo estimation by incorporating thermodynamic as well as aerodynamic effects [4]. However, input data needed by the FAO-56 method includingtemperature, relative humidity, solar radiation, wind speed, and more information about the area make its applicability challenging for several locations across the globe. Solutions to this problem have been sought by introducing various empirical methods through simplifying the FAO-56 method; such as, Hargreaves equation that requires temperature data only to estimate ETo. The choice of methods solely depends upon the accuracy of methods and availability of reliable data. An ideal method, however, should be based on minimal input data variables with no compromise on precision and accuracy [5]. Artificial neural networks (ANNs) have drawn the attention of researchers to model several complex non-linear hydrological relationships. Several ANNs have been successfully used to solve hydrology related problems, such as river flows extrapolation [6], rainfall run-off modeling [7], sediment forecasting [8], and notably ETo [9]. Afterwards, several improvements in ANN's architecture, learning algorithms have been proposed by different researchers. For example, [PERSON] et al. [10] modeled the ETo for rice crop. They used radial basis neural networks with varied combinations of climatic input variables. [PERSON] et al. [11] proposed the explicit neural network to model ETo by using daily climatic variables in California, USA after comparing six different conventional methods. [PERSON] [12] trained the ETo models by using only the air temperature of the Caspian Sea in North Iran and compared the results with the FAO-56 model. He concluded that the air temperature was able to explain the variability in ETo without compromising the accuracy of results. In recent years, several other machine learning models were tested to estimate ETo such as extreme learning machines [2; 13; 14], support vector machines [10], and fuzzy genetic approach [15]. Several climatic variables such as temperature, relative humidity, and ETo exhibit seasonality and may be treated as a time series problem. Although, ANNs handle the non-linear behavior of time series better than regular regression; however, most of the ANNs do not explain seasonality and time dependence. Simple ANN architecture such as multilayer perceptron does not contain any memory blocks to store the previous information for better prediction. To address this issue, recurrent neural networks (RNNs) were introduced to capture the dynamics of sequences via cycles in the network of nodes [16]. In RNNs, the temporal relations of inputs are addressed by feedback connections for maintaining internal memory states. Recurrent neural networks have proved to be effective in learning time dependent signals for short-term structures [17]. They are capable of storing the previous records in their memory. However, in large time series sequences, the vanishing gradient hampers the learning of these models. This problem occurs when the gradient updates become very small and add no major contribution towards model learning.