Bridging computational power and environmental challenges: a perspective on neural network predictive models for...

Area of Science:

• Environmental Science
• Artificial Intelligence
• Earth System Science

Background:

• Extreme environmental events are increasing in frequency and severity.
• Current reactive management strategies are insufficient.
• A proactive approach to environmental management is critical.

Purpose of the Study:

• To highlight artificial neural networks (ANNs) as a transformative tool for environmental forecasting.
• To address challenges hindering ANN integration into decision-making.
• To propose a roadmap for utilizing ANNs to build environmental resilience.

Main Methods:

• Reviewing current advancements in ANNs for environmental applications.
• Synthesizing the role of physics-informed neural networks (PINNs) and explainable AI (XAI).
• Proposing an interdisciplinary roadmap for ANN implementation.

Main Results:

• ANNs demonstrate superior predictive performance in hydrology, air quality, and ecology.
• PINNs and XAI can bridge the gap between data-driven insights and physical plausibility.
• Challenges include data quality, model interpretability, and interdisciplinary collaboration.

Conclusions:

• ANNs hold significant potential for enhancing environmental forecasting and management.
• Overcoming integration challenges requires interdisciplinary collaboration and advanced AI techniques.
• A structured roadmap is proposed to translate ANN potential into actionable tools for environmental resilience.