## 4 Devised Configuration for the Integrated WT-ACO-FFANN Based ML Forecast Model
### Feedforward Artificial Neural Network (FFANN)  
ANN is a robust data processing (regression or classification) model, which is able to capture an existing complex relationship within a dataset. ANN can quickly learn the behavior of the data and acquire knowledge from them. The development of the ANN model was motivated by the method the human nervous system, specially the brain, employs to process facts. The important characteristic of the ANN is its unique configuration for data manipulation. It consists of plenty of information manipulating constituents known as neurons, which are configured in various hierarchical layers. The layers are input, hidden, and output layers. The neurons making up the input, hidden, output layers are respectively called input, hidden, and output neurons. Neurons are connected through certain scaled connections. The neuron connecting scales are known as weights. The neurons in ANN operate in combination to solve a given real-world problem (fitting, approximation, regression, pattern recognition, classification, etc.) [19; 34; 35]. ANN learns about the environment (data behavior) through examples or experiences as human beings do. ANNs are implemented to solve a given problem or application via a learning or training procedure. Training a human brain is performed via sufficiently updating activities or rules of the synaptic networks among neurons in the brain nervous system. The same is done for training the ANN neurons. ANNs learn by updating the connection weights among the neurons. There are different types of ANNs based on the configuration (connection arrangement) of the neurons and the flow of information. ANNs can be categorized as feedforward artificial neural network (static network) and recurrent artificial neural network (dynamic network). The FFANNs have no feedback components and hold no delays. In FFANN, information flows right from inputs to output in the forward direction. The model output is computed right from the input employing the feedforward connection weights, while in the recurrent neural network models, the output relies not only on the present inputs to the model but also on the present or the prior input, output, or state of the model. Although they are effective for high dimensions and very complex problems, the recurrent neural networks are complex for implementation and can be computationally exhaustive. On the other hand, FFANNs are easy to implement, fast, and very effective for reduced-dimension data processing problems such as forecasting. While developing a FFANN model for solving a specific problem, the quantity of the hidden layer neurons should be chosen properly and with great care. Nevertheless, there exists no clear technique for optimal sizing of the amount of neurons at hidden layers. In this study, we determine the quantity of hidden neurons through extensive and continuous experimentation, which is called the empirical parametrization process. Various configurations of FFANN with plenty of neurons at the hidden layer are examined. The best FFANN model configuration is selected using the root mean squared error (RMSE) measure between the model output value and the target real value. The selected configuration type and parameters values of the FFANN model for the proposed WPP problem in this paper are given in Table 2. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of iterations/generations & 1000 \\ Number of variables, \(n\) & Number of FFANN neuron connection weights \\ Number of Ants, \(m\) & 15 \\ Initialization of pheromone, \(\xi_{0}\) & 0 \\ Information heuristic weight, \(\alpha\) & 1.0 \\ Expectation heuristic weight, \(\beta\) & 1.0 \\ Pheromone strength, \(Q\) & 2 \\ Pheromone evaporation ratio, \(p\) & 0.90 \\ \hline \end{tabular}
\end{table} 
Table 1: ACO parameters and their values.