## 1 Introduction  
Blasting serves as a fundamental technique for rock breakage in both mining and civil engineering ventures worldwide, owing to its efficiency and cost-effectiveness compared to alternative methods. It enables the successful completion of various large-scale projects, rendering them technically and economically feasible [1]. Despite its effectiveness, blasting operations release only a fraction of the explosive energy towards actual rock breakage and displacement, with the remaining energy contributing to undesirable environmental effects such as ground vibration, noise, fly rock, and noxious gases [2; 3]. These repercussions can extend to reserved slopes and structures, potentially compromising their structural integrity. Among the detrimental effects of blasting, ground vibration stands out as a major concern for mine planners, designers, and environmentalists [3; 4]. Ground vibration manifests as a wave motion, propagating outward from the blasting source akin to ripples spreading across a water surface after an object impact. Peak particle velocity (PPV) serves as the primary parameter for evaluating blast-induced ground vibration, according to regulations governing blasting practices and research on blast-induced ground vibration [5]. The archived surveys and obtained results demonstrated that PPV can cause structural damage to buildings, bridges, and other infrastructure in the vicinity; disturb sensitive ecosystems, such as nearby wildlife habitats or aquatic environments; cause settlement or subsidence of the ground, particularly in areas with loose or uncompated soil; disturb underground aquifers or disrupt nearby water bodies, leading to contamination from pollutants or sedimentation; and promote rockburst in deep openings [6; 7; 8; 9; 10; 11]. Efforts to quantify blast-induced ground vibration based on PPV have resulted in three main categories: field measurement, empirical equations, and computational methods (including finite element and artificial intelligence techniques). While field measurementsoffer accuracy, they are costly, time-consuming, and site-specific, leading engineers to rely predominantly on traditional empirical models. Several researchers have proposed variants of traditional empirical models, such as those by [PERSON] et al. [12], [PERSON] and [PERSON] [13], [PERSON] and [PERSON] [14], and others [15; 16]. Traditional empirical models have paved the way for intelligent algorithmic predictions, with recent studies showcasing the efficacy of techniques like modified Kuz-Ram models and comparisons between empirical formulations and intelligent algorithms [17]. However, these equations, while simple to implement, are known to be inaccurate and site-specific, rendering them unusable for rocks with similar characteristics to those used in developing the equations. Over the years, researchers have sought to overcome the limitations of conventional empirical equations by employing advanced soft computing analytics to describe complex real-world phenomena. Techniques such as artificial neural networks (ANNs), fuzzy models, linear regression, decision trees, random forests, and deep learning have been utilized to predict and estimate blast-induced vibrations [18; 19; 20; 21; 22; 23; 24; 25]. Notable studies by Refs. [23; 24; 25] have focused on predicting ground vibration induced by blasting using ANN methodologies, categorizing their models based on the parameters employed. Additionally, research has explored various machine learning (ML) approaches, with ANNs, support vector machines (SVMs), and adaptive neuro-fuzzy inference systems (ANFIS) emerging as prominent algorithms [18]. Hybrid models combining multiple ML algorithms have shown promise in enhancing prediction accuracy, although they often present complex mathematical formulations [18; 26]. In open-pit mining, there is an increasing trend towards using intelligent algorithms for predictive and optimization techniques to address blast-induced ground vibration.