## 1 Introduction
All these reasons hinder the implementation of this method in certain EMF estimation scenarios and encourage researchers to explore or develop alternative methods. Artificial intelligence (AI) techniques have been promoted among engineering and physics researchers since the early years of this century [26; 27]. These techniques can be useful for EMF estimation due to their ability to handle and learn from complex datasets, provide accurate predictions, and offer several advantages over traditional approaches [28; 29]. They also can be retrained by more datasets to update the model to enhance various conditions coverage or improve the accuracy of the model. One of the most renowned types of AI for engineering and physics problems is artificial neural networks (ANN), which has been inspired by the human brain's learning and decision-making processes [19]. The flexibility and adaptability of ANNs enable them to handle small and large datasets and extract meaningful insights from vast amounts of data/information [30]. Also, ANN's capability of handling non-linear relationships makes them particularly advantageous in addressing complex and dynamic problems [27]. Moreover, they are able to predict the target parameters with remarkably high accuracy in a noticeably short timeframe. Feed-forward neural network (FFNN) is one of the most common ANN methods among researchers due to its simplicity and high accuracy [13]. Cascade-forward neural network is a variant of ANN that is a more complex version of FFNN due to connecting the input and hidden layers to all preceding layers [31; 32]. Due to its naturally complex design, in some cases, it can yield more accurate results than a simple FFNN [33]. In the most recent decade, researchers tried to implement AI methods to predict electric or magnetic fields. In [34], [PERSON] et al. initially provided a setup to measure the EMF to make a dataset for the AI model. Then, they developed a multilayer FFNN model to predict the EMF radiation via electrostatic discharges. The relative error between the predicted and actual value for EMF was reported between 5.437% and 23.620%. In [13], [PERSON] et al. used a simple multilayer perceptron (MLPNN) and a generalized regression neural network (GRNN) model to predict electric and magnetic fields. They proposed several models for both electric and magnetic fields, each one considering only one longitude position of the conductor. The performance of MLPNN and GRNN models using the Root Mean Squared Error (RMSE) value as the index was reported as 0.030855 and 0.053084 for the electric field and 0.02719 and 0.03666 for the magnetic field, respectively. In [35], [PERSON] et al. implemented single and double-layer models based on FFNN to predict magnetic fields for four substations in Brunei. They trained the models for each of these substations separately and the results indicated that the R-squared value range of their models was from 70.9039% to 98.881%. In [20], [PERSON] et al. suggested a model using a cuckoo search algorithm (CSA) and neuro-fuzzy controller (NFC). First, they used the cuckoo search algorithm as an optimizer to optimize the conductor spacing, which has a significant effect on the intensity of EMF to make an input dataset with minimum electric field intensity for the NFC. This is because they generated the training data using some base equations while considering some simplifications to be able to use those formulas. Finally, by training the NFC with that dataset, they reached a model that was able to estimate the intensity by 5-190% relative error for different data points. In [30], [PERSON] et al. implemented two algorithms, namely the charge simulation method and Biot-Savart law, to generate target values for the electric field intensity and magnetic flux density datasets, respectively. After that, they developed a FFNN model using a Scaled Conjugate Gradient (SCG) as the training function. In another paper with the same process for data collection, [PERSON] et al.