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Environmental Influences on Intelligence

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Despite the strong genetic influence on traits like intelligence, environmental factors significantly shape outcomes. For example, while over 90% of height variation is due to genetic differences, environmental factors such as nutrition also have a notable impact. Similarly, for intelligence, changes in a child's surroundings can significantly alter their IQ. Research shows that enriched environments boost children's academic success and help them develop key cognitive skills. Children...

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Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source

Haoli Xu^1,2,3, Xing Yang^1,3, Yihua Hu^1,3

¹State Key Laboratory of Pulsed Power Laser, College of Electronic Engineering, National University of Defense Technology, Hefei, 230037, China.

Environmental Science and Ecotechnology

|September 17, 2024

Summary

This summary is machine-generated.

This study introduces an accurate and explainable artificial intelligence (AI) transformer model for environmental assessments. The AI model identifies key environmental indicators, enhancing trust and enabling targeted management strategies.

Keywords:

Explainable AI Intelligent environmental assessment Multi-source data Transformer

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Area of Science:

Environmental Science
Artificial Intelligence
Machine Learning

Background:

Environmental assessments are vital for sustainable development.
Traditional AI models in environmental assessments lack transparency, hindering trust despite high accuracy.
Explainable AI (XAI) is needed to bridge the gap between AI performance and environmental governance.

Purpose of the Study:

To evaluate a transformer model's performance against other AI approaches for environmental assessments.
To apply saliency maps for explainability in AI-driven environmental assessments.
To identify key environmental indicators influencing AI predictions.

Main Methods:

Utilized extensive multivariate and spatiotemporal environmental datasets.
Compared a transformer model with other AI methods.
Employed saliency maps to analyze indicator contributions to AI predictions.

Main Results:

The transformer model achieved approximately 98% accuracy and an AUC of 0.891.
Regional assessments indicated varying environmental levels (II-V) across study areas.
Water hardness, total dissolved solids, and arsenic concentrations were identified as the most influential indicators.

Conclusions:

The developed AI model is accurate, explainable, and provides actionable insights for environmental management.
This study advances AI application in environmental science by offering a trustworthy, robust, and interpretable model.
The findings enhance understanding and trust in AI-assisted environmental assessments and governance.