## 1 Introduction
As a result, researchers have suggested integrating intelligent algorithms and additional influencing factors into prediction models to improve accuracy [27; 28; 29]. Recent endeavors have concentrated on enhancing prediction models through hybrid approaches like cuckoo optimization and particle swarm algorithms, resulting in better PPV predictions [30; 31; 32]. Moreover, preprocessing input data using methods such as clustering and feature selection has demonstrated potential in boosting model performance [33; 34; 35]. Metaheuristic algorithms play a vital role in optimizing hybrid models for blast vibration prediction and mine-blasting optimization [30; 31; 32; 36; 37; 38]. For instance, the grey wolf optimization (GWO), hunger games search (HGS), manta ray foraging optimization (MRFO), aquila optimization (AO), the bat-inspired algorithm (BA), and the naked mole-rat algorithm (NMRA) have been utilized to predict blast-induced ground vibration [36; 39] under the integration of ANN models, while the whale optimization algorithm (WOA), grey wolf optimization (GWO), and Bayesian optimization algorithm (BO) have been applied to effectively optimize the hyper-parameters of the XGBoost model for predicting blast-induced ground vibration [40]. [PERSON] et al. [41] also utilized various metaheuristic algorithms to optimize another machine learning model (i.e., SVR), such as the firefly algorithm (FA), the genetic algorithm (GA), and particle swarm optimization (PSO). Existing methodologies, such as empirical equations and traditional prediction models, often lack the accuracy and flexibility required to effectively capture the complex dynamics of blast-induced ground vibrations. While some studies have attempted to address this by employing advanced soft computing techniques like artificial neural networks (ANNs) and machine learning algorithms, many still fall short in achieving optimal prediction accuracy. Additionally, some models may provide impractical mathematical formulations or require significant manual tuning, making them less suitable for real-world applications. In light of these limitations, there is a pressing demand for a novel and robust predictive model capable of accurately forecasting PPV while circumventing the constraints of current methodologies. Thus, this study introduces a pioneering chaos game optimization (CGO)-ANN model, which integrates CGO, a metaheuristic optimization technique, with the ANN model. This hybrid approach capitalizes on the advantages of both methodologies, employing CGO to optimize the parameters of the ANN model and enhance its predictive efficacy. Furthermore, the PSO and GA algorithms were employed to optimize the ANN model, and a comparison was made between the CGO-ANN model, a single ANN, and USBM empirical models in PPV prediction. By introducing the CGO-ANN model, this study seeks to rectify the limitations of prior approaches and offer a more precise and dependable tool for PPV prediction in mine blasting scenarios. Through rigorous testing and validation, the proposed CGO-ANN model endeavors to demonstrate its superiority over existing methodologies, providing enhanced predictive accuracy and practical utility in real-world mining contexts.