Geology Reference
In-Depth Information
Fig. 8.10 Percentage changes in number of
flood events predicted by different models
Fig. 8.11 CDF of generalized Pareto distribution (GPD) generated by Statistical Blockade
8.3.3 Application of Arti
cial Neural Network
The story of ANNs started in the early 1940s when McCulloch and Pitts [
10
]
developed the
first computational representation of a neuron. Later, Rosenblatt [
11
]
proposed the idea of perceptrons and single layer feed-forward networks of
McCulloch
Pitts neurons, and focused on computational tasks with the help of
weights and training algorithm. The applications of ANNs are based on their ability
to mimic the human mental and neural structure to construct a good approximation
of functional relationships between past and future values of a time series. The
supervised one is the most commonly used of the ANNs, in which the input is
presented to the network along with the desired output, and the weights are adjusted
so that the network attempts to produce the desired output. There are different
learning algorithms and a popular algorithm is the back propagation algorithm that
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