ANNs are based on a series of parallel architectures that are connected by nodes called artificial neurons [

23,

24,

25]. These networks use learning capabilities obtained from inputs, which can be effectively used for the prediction of mean sea level as available data are fairly large. One of the main strengths of the neural network architecture is that it improves its own problem-solving ability by continually learning from trial and error. Once this is done over time, the network is able to detect patterns and processes in the data. An artificial neuron contains five main components: inputs, weights, sum function, activation function and outputs [

26]. In this network, units are placed as layers that are connected to allow the information to flow unidirectionally. It passes from the input units through the units located on the hidden layers and then to the units on the output layer [

27]. A set of weighted inputs allows each artificial neuron in the system to give related outputs. The effect of the weights is calculated by the sum function which is calculated by Equation (4).

where “(

net)

_{j} is the weighted sum of the

jth neuron for the input received from the preceding layer with

n neurons,

w_{ij} is the weight between the

jth neuron in the preceding layer,

x_{i} is the output of the

ith neuron in the preceding layer,

b is a fixed value as internal addition and Σ represents the sum function” [

26,

28,

29]. The weights provide an important link for the ANN memory and significant information is fed through the network for optimization by backward propagation [

30]. These weights are changed as the input values are read by the network to reduce the difference between the predicted and target values. The activation function processes the net input obtained through the sum function and provides the output values. The output is created using a sigmoid function as follows:

where

α is a constant used to control the slope of the semi-linear region [

26].

The sigmoid function is used in the ANN algorithm to convert the linear inputs into non-linear signals. This is extremely important for the learning of higher order polynomials beyond one degree for deeper networks [

31,

32]. The differentiable nature of the activation function enables the much needed backpropagation process which otherwise will not be possible.