## 1 Introduction  
Air-overpressure (AOp) or airblast is an unwelcome outcome of blasting in mining operations. The blasting creates temporary air blast pressure waves that persist for some time [1, 2]. More than 20% of the explosive energy is used to fracture and replace the rock fragments. More than 70% of this energy is dissipated, which causes AOp and other unwanted phenomena [1]. Various parameters, including terrain circumstances, blast design, and climate, are influential on AOp [2, 3]. The enormous shock waves coming out of the blast spot toward the free facade create AOp. Hence, the AOp can be defined as a shock wave deflected laterally by density changes in the air. These AOp waves are released with some audible high- or low-frequency sounds. AOp can cause structural damage and harm to people in the vicinity of quarty sites [4].  
Several studies attempted to establish associations for the AOp forecast using its influential factors. [PERSON] et al. [5] ascertained an experimental association between AOp and interval among blast planes and monitoring spot and mass of explosive substances (32%). To reproduce ground shock and air explosion tensions deriving from facade explosions,Wu and [PERSON] [6] connected Autodyn2D to mathematical models, wherein properties of rock materials and free air were involved. A partial-empirical model for forecasting the airway tension caused by blasting operations outside a tunnel was developed by [PERSON] et al. [7]. They claim that their model works in different situations. [PERSON] et al. [8] proposed a photometric curve and iso-attenuation curves to describe the phenomenon, as well as a proposed charge-distance curve to resolve the query.  
Recently, various investigations have applied machine learning (ML) techniques to resolve science and engineering problems [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. Such techniques were used to predict the AOp values and identify the most influential predictors [20; 21; 22]. These techniques are both time and cost-saving and can help both researchers and practitioners allocate resources to other necessary operations. Among the ML algorithms, artificial neural networks (ANNs) and an adaptive neuro-fuzzy inference system (ANFIS) were employed more frequently than other techniques in the investigations of AOp [23]. Some studies used tree-based techniques such as XGBoost, random forest (RF), and M5 [24]. Rare studies employed support vector regression to predict the AOp [25]. Additionally, some of these models were optimized with some techniques to improve accuracy and efficiency. Some of these optimization techniques include genetic algorithms (GA) and particle swarm optimization (PSO) [2]. Lastly, different methods, such as fuzzy Delphi methods, were used to choose the inputs before the model was built [26].  
Despite the vast application of ML algorithms for AOp prediction, various efficient models for prediction, optimization, and input selection are neglected. In this study, the authors developed a novel prediction model that encompasses SVR as a prediction model, the Grasshopper algorithm for optimizing the SVR's hyperparameters, and the Boruta algorithm (BFS) for input selection. The Grasshopper algorithm is used in this study because it is easy to use, has a structure without gradients, avoids local optimums well, and treats problems as black boxes. SVR is rarely employed for AOp prediction, and to the best of the authors' knowledge, the Grasshopper and Boruta algorithms have not been applied in this domain. The rest of this paper is structured as follows: the next section discusses algorithms used in this study, including SVR, GO, and BFS. Additionally, the case study of this research will be explained in this section. The results and discussions' section describes the data preparations and performance criteria. The results of input selection and model optimization are reported in this section. The paper ends with a conclusion that sums up what was learned from this study and makes some suggestions for future research.