Advanced Evapotranspiration Methods and Applications

adopts a "Brain metaphor" of information processing. Information processing in a neural network occurs through interactions involving large number of simulated neurons. A neural network (NN) is a simplified model of the human brain nervous system consisting of interconnected neurons in a parallel distributed system, which can learn and memorize the information. In NN, the interneuron connection strengths, known as 'synaptic weights' are used to store the acquired knowledge [32]. In other words, ANN discovers the relationship between a set of inputs and desired outputs without giving any information about the actual processes involved;

Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks

DOI: http://dx.doi.org/10.5772/intechopen.81369

it is in essence based on pattern recognition. ANNs consist of a number of

the researchers.

Figure 2.

27

A nonlinear model of a neuron.

4.1 Model of a neuron

whether it is positive or negative, respectively.

interconnected processing elements or neurons. How the inter-neuron connections are arranged determines the topology of a network. A neuron is the fundamental unit of human brain's nervous system that receives and combines signals from other neurons through input paths called 'dendrites'. Each signal coming to a neuron along a dendrite passes through a junction called 'synapse', which is filled with neurotransmitter fluid that produce electrical signals to reach to the soma or cell body where processing of the signals occurs [16]. If the combined input signal after processing is stronger than the threshold value, the neuron activates, producing an output signal, which is transferred through the axon to the other neurons. Similarly, ANN consists of a large number of simple processing units called neurons (or nodes) linked by weighted connections. A comprehensive description of neural networks was presented in a series of papers [33–35], which provide valuable information for

The main function of artificial neuron is to generate output from an activated

nonlinear function with the weighted sum of all inputs. Figure 2 illustrates a nonlinear model of a neuron, which forms the basis for designing ANN. The input layer neurons receive the input signals (xi) and these signals are passed to the cell body through the synapses. A set of synapses or connecting links is characterized by its own weight or strength. A signal at the input of synapse 'i' connected to neuron 'k' is multiplied by the synaptic weight 'wki'. The input signals, weighted by the respective synapses of the neuron are added by a linear combiner. An activation function or squashing function is used for limiting the permissible amplitude range of the output of a neuron to some finite value. An external bias (bk) has an effect of increasing or decreasing the net input of the activation function depending on

#### Figure 1.

Geographical locations of study sites in India.


#### Table 1.

Station locations and period of records.
