🌟 Explainable AI Models for Blast-Induced Air Overpressure Prediction Incorporating Meteorological Effects 💥🌦️

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Blast-induced air overpressure is a critical safety concern in mining, construction blasting, and defense operations 💣. Accurate prediction of air overpressure helps prevent structural damage, environmental impact, and human discomfort 🏗️👷‍♂️. Traditional empirical models often fail to capture complex nonlinear interactions between blast parameters and atmospheric conditions 🌍. Explainable Artificial Intelligence (XAI) emerges as a powerful solution by combining predictive accuracy with interpretability 🤖✨.


🧠 Role of Explainable Artificial Intelligence (XAI)

Explainable AI focuses on transparent machine learning models that reveal how and why predictions are made 🔍📊. Unlike black-box models, XAI techniques such as SHAP, LIME, and decision trees allow engineers to understand the influence of input variables on air overpressure outcomes 🧩. This enhances trust, reliability, and regulatory acceptance of AI-driven blast prediction systems ✔️.

💥 Blast-Induced Air Overpressure Prediction

Air overpressure results from the rapid release of energy during blasting operations ⚡. Key blast parameters include charge weight, distance, delay timing, and blast geometry 🎯. AI models such as Random Forest, Gradient Boosting, and Neural Networks are capable of modeling these complex relationships with high precision 📈. When enhanced with explainability, these models provide actionable insights for safer blast design 🛠️.

🌦️ Incorporation of Meteorological Effects

Meteorological factors play a significant role in air overpressure propagation 🌬️. Parameters such as wind speed, wind direction, temperature, humidity, and atmospheric pressure strongly influence blast wave travel 🌡️🌪️. Integrating real-time weather data into AI models improves prediction accuracy and situational awareness 📡. XAI methods further clarify how each weather variable amplifies or attenuates air overpressure levels 🌈.

📊 Model Interpretation and Decision Support

Explainable outputs help identify dominant factors affecting overpressure, enabling engineers to optimize blasting schedules and designs 🧠📉. Sensitivity analysis and feature importance rankings assist in proactive risk mitigation 🚨. Visualization tools derived from XAI enhance communication between technical experts, regulators, and field operators 🗣️📋.

🌱 Future Scope and Sustainability

Explainable AI-based blast prediction systems contribute to sustainable and intelligent mining practices ♻️. They support safer operations, reduced environmental impact, and data-driven decision-making 🌍✨. Future research can explore hybrid AI-physics models and real-time adaptive systems for enhanced blast control 🚀.

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