ABSTRACT

Evaporation is a complex and nonlinear phenomenon because it depends on several interacting climatological factors, such as temperature, humidity, winds speed, bright sunshine hours, etc. An Artificial Neural Networks (ANN) is a flexible mathematical structure, which is capable of identifying complex nonlinear relationships between input and output datasets. The ANN models have been found useful and efficient, particularly in problems for which the characteristics of the processes are difficult to describe using physical equations. An ANN model can compute complex nonlinear problems, which may be too difficult to represent by conventional mathematical equations. These models are well suited to situations where the relationship between the input variable and the output is not explicit. Instead, ANN, map the implicit relationship between inputs and outputs through training by field observations. The model may require significantly less input data than a similar conventional mathematical model, since variables that remain fixed from one simulation to another do not need to be considered as inputs. The ANN is useful, requiring fewer input and computational effort and less real time control. An ANN can quickly present sensitive responses to tiny input changes in a dynamic environment. ANN are effective tools to model nonlinear systems [52-81]. A neural network model is a mathematical construct whose architecture is essentially analogous to the human brain. Basically, the highly interconnected processing elements, arranged in layers are similar to the arrangement of neurons in the brain. The ANN has found successful applications in the areas of science, engineering, industry, business, economics and agriculture. Recently, ANN have been applied in meteorological and agro ecological modeling and applications [37]. Most of the applications reported in literature concern estimation, prediction and classification problems. Neural network applications have diffused rapidly due to their functional characteristics, which provide many advantages over traditional analytical approaches [38-57].