ENTERPRISE AI ANALYSIS
Environmental education as a means of combating growing environmental pollution: an optimized- explainable artificial intelligence (XAI) approach
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Executive Impact Summary
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Deep Analysis & Enterprise Applications
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The study employs a quantitative methodology, utilizing statistical techniques and AI models to analyze survey data from 402 students. This approach allows for the identification of correlations and predictive insights regarding environmental education's impact on pollution awareness and mitigation efforts. The use of explainable AI (XAI) enhances the interpretability of complex relationships, providing a robust framework for evidence-based decision-making in environmental education planning.
This research pioneers the use of an optimized-explainable artificial intelligence (XAI) approach, specifically leveraging Gaussian Process Regression with Bayesian Optimization (GPR-BO-M2). This advanced hybrid model demonstrated superior predictive performance (R²=0.951) compared to traditional models, effectively identifying key influential variables and providing a robust framework for understanding and combating environmental pollution through education.
Superior Predictive Accuracy
95.1% Achieved by GPR-BO-M2 model (R² value)Enterprise Process Flow
Data Collection (Survey)
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Pre-analysis (Statistical & XAI)
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Model Training (GPR-BO-M2)
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Performance Evaluation (R², RMSE, MAE)
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Policy Recommendation
| Model Type | Key Advantages | Limitations | Best Use Case |
|---|---|---|---|
| Traditional ANN/BT |
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Preliminary analysis, smaller datasets, less complex relationships |
| Optimized GPR-BO-M2 |
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High-stakes predictive modeling, policy development, complex environmental education interventions |
Libyan Universities: A Case for AI-Driven EE
This study focused on two Libyan universities: Omar Al-Mukhtar University and Qubba Branch, University of Derna. With 402 student respondents, the research demonstrates a significant opportunity for integrating AI-driven environmental education strategies. The findings highlight the students' knowledge and concern regarding environmental pollution, suggesting that targeted, AI-optimized interventions can be highly effective in fostering proactive environmental behaviors within similar socio-environmental contexts. The application of GPR-BO-M2 provides a robust framework for developing personalized learning pathways and efficient resource allocation in environmental education initiatives in Libya.
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