## 5 Conclusions  
In this study, we explored various predictive models for estimating PPV in blast-induced ground vibration scenarios. By leveraging machine learning techniques and empirical modeling, we aimed to develop robust models capable of accurately predicting PPV, a critical parameter for assessing the potential impacts of blasting operations on surrounding structures and the environment. Our analysis revealed compelling insights into the performance of different predictive models. Among the models evaluated, the proposed CGO-ANN model emerged as a stand-out, demonstrating superior predictive accuracy and reliability compared to alternative optimized models, including GA-ANN and PSO-ANN. The CGO-ANN model, rooted in chaos theory, proved particularly adept at capturing the uncertainties inherent in PPV propagation through the ground, showcasing its potential as a novel approach for PPV prediction. Furthermore, our study highlighted the limitations of traditional empirical models, such as the USBM model, in accurately predicting PPV. Despite its widespread use, the USBM model exhibited low reliability in our analysis, reaffirming the need for more advanced modeling techniques to address the complexities of blast-induced ground vibration. Finally, our study contributes to the advancement of blast management practices and environmental stewardship by offering improved methodologies for assessing and mitigating the impacts of blasting operations. Moving forward, further research efforts should focus on refining and validating predictive models, incorporating additional factors influencing PPV, and conducting field validation studies to enhance the applicability and robustness of PPV prediction models in real-world scenarios. Although the obtained results are promising, there are several limitations to consider. The study primarily focuses on PPV prediction in blast-induced ground vibration scenarios, potentially limiting its applicability to other types of vibration prediction or mining-related parameters. Additionally, the dataset used for model development and evaluation is derived from a single mining site, which may not fully represent the diversity of blasting conditions and geological settings encountered in other mining operations. Furthermore, the analysis may overlook certain factors influencing PPV that were not included in the dataset, such as variations in geological structures, weather conditions, or blasting techniques. In light of these limitations, future research directions should include investigating the applicability of the CGO-ANN model and other predictive models in different mining sites and geological contexts to assess their generalizability. Exploring the incorporation of additional variables or features into the predictive models to enhance their accuracy and robustness, considering factors like geological properties, weather conditions, and blast design parameters, is also warranted. Additionally, conducting field validation studies to validate the performance of predictive models in real-world blasting scenarios and assess their practical utility and reliability will be beneficial. Conceptualization, S.Z. and M.C. ; Methodology, S.Z., L.W. and M.C. ; Formal analysis, L.W. ; Investigation, M.C. ; Data curation, S.Z. ; Writing--original draft, S.Z., L.W. and M.C. ; Writing--review & editing, S.Z., L.W. and M.C. ; Visualization, L.W. All authors have read and agreed to the published version of the manuscript. * [1] [PERSON]; [PERSON] Evaluation of parameters affected on the blast induced ground vibration (BIGV) by using relation diagram method (RDM). _Acta Montan. Slovaca_**2010**, _15_, 261. * [2] [PERSON] Evaluation and prediction of blast-induced ground vibration using support vector machine. _Int. J. Rock Mech. Min. Sci._**2010**, _47_, 509-516. [CrossRef]
* [3] [PERSON] Rock breakage by explosives. In _Gasdynamics of Explosions and Reactive Systems_; Elsevier: Amsterdam, The Netherlands, 1980; pp. 329-340.