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

## TL;DR

This paper explores how artificial neural networks can help predict and manage environmental challenges by offering proactive solutions.

## Contribution

The paper introduces a roadmap for integrating neural networks into environmental decision-making through interdisciplinary collaboration and new modeling approaches.

## Key findings

- Artificial neural networks excel in predicting environmental phenomena due to their ability to handle complex dynamics.
- Physics-informed and explainable AI approaches are key to making neural network predictions trustworthy and actionable.

## Abstract

The escalating frequency and severity of extreme environmental events underscores the critical need for a paradigm shift from reactive to proactive management strategies. This perspective article argues that artificial neural networks (ANNs) represent a transformative tool for environmental forecasting, capable of capturing the non-linear, high-dimensional dynamics that define complex Earth systems. While ANNs demonstrate superior predictive performance across domains such as hydrology, air quality, and ecology, their integration into decision-making workflows remains hindered by challenges related to data quality, model interpretability, and a lack of interdisciplinary collaboration. We synthesize current advancements, highlighting the pivotal role of physics-informed neural networks (PINNs) and explainable AI (XAI) in bridging the gap between data-driven insights and physical plausibility. Finally, we propose a concrete interdisciplinary roadmap, encompassing curated benchmarks, hybrid modeling, educational initiatives, and institutional co-design, to translate computational potential into trustworthy, actionable tools for building environmental resilience.