## 1 Introduction
### Artificial Intelligence and Softcomputing Approach  
Over the last few years, researchers have employed various soft computing (SC) techniques to forecast flyrock distance, in addition to traditional empirical methods. As a pioneering effort towards implementation of AI techniques in flyrock prediction, [PERSON] et al. [27] developed a back propagation ANN model with an 8-3-3-2 neuron topology. The model was used to predict the degree of rock fragmentation and flyrock resulting from blasting in an iron mine in Iran. The authors later proposed a hybrid neuro-genetic algorithm for predicting flyrock and back-break at the Sungun Copper Mine [28]. This involved a genetic algorithm to optimize various parameters of the neural network such as the number of neurons in the hidden layer, learning rate, and momentum. Subsequently, many research works included the integration of an ANN with various other algorithms such as the Fuzzy Interface System (FIS) [29], Particle Swarm Optimization (PSO) [30], Imperialist Competitive algorithm (ICA) [31], and Firefly Algorithm (FA) [32], among others, to overcome the limitations of classic ANN modeling in predicting flyrock distance in copper mines and aggregate rock quarries. Following this approach, an adaptive neuro-fuzzy inference system (ANFIS) was employed by [PERSON] et al. [33] to estimate blast-induced flyrock distance with a dataset of 125 blasts from four different opencast limestone mines in India. They found that ANFIS outperformed ANN and MVRA as a predictive tool for complex flyrock distance prediction problems of opencast mines. In an attempt to estimate flyrock distance at the Delkan iron ore mine in Iran, [PERSON] et al. [34] constructed several GP and GEP models using a dataset of 97 observations, comprising five input variables and one output variable, and concluded that the GEP model was highly reliable to predict flyrock. An advanced version of ANN, known as a deep neural network (DNN) model, was developed by [PERSON] et al. [35] to predict flyrock based on the data from 240 blast events at Ulu Thiram quarry, Malaysia, The study utilized a powerful optimization technique called whale optimization algorithm (WOA) to minimize the flyrock. Recently, [PERSON] et al. [36] developed an outlier robust ELM (ORELM) model to predict the distance of flyrock caused by blasting in three aggregate rock quarry sites in Johor, Malaysia, and recommended the use of evolutionary optimization algorithms such as PSO and GA to further improve the performance of the ELM and ORELM models during the training process. The study used a dataset of 82 bench blasts and compared the results with those from multiple linear regression (MLR) models to discover that ORELM models were more efficient for predicting flyrock than both ANN and MLR models. Further,Bhatawdekar et al. [37] applied a novel equilibrium optimization technique to the ELM to anticipate the extent of flyrock generated in 114 blasts conducted at an aggregate limestone quarry in Thailand. In addition, two hybrid prediction models, namely, PSO-ANN and PSO-ELM, were developed and all models were run ten times. Comparing the EO-ELM model to the other two, it outperformed them in average results and exhibited a superior convergence in 10 runs. Notably, the study's applicability was limited to the site only and may not be generalizable for other geological settings. Table 1 presents a compilation of some of the latest implementations of AI and SC methods for the prediction of flyrock in surface mines, summarizing the utilized algorithms, predicted variables, and the quantity of datasets employed by various researchers. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Algorithm** & **Input/Predictors** & **Target Variable** & \begin{tabular}{c} **Location/Region of** \\ **the Sites Studied** \\ \end{tabular}  & \begin{tabular}{c} **R\({}^{2}\) for the Testing** \\ **Dataset** \\ \end{tabular}  &