Monitoring Environmental Problems Using AI-PoweredTools and Proposing Solutions

This chapter systematically reviews the application of Artificial Intelligence (AI) tools in monitoring environmental problems and proposes solutions for their effective implementation. It examines several critical domains: air quality forecasting, water resource and quality monitoring, soil heavy metal contamination assessment, biodiversity and wildlife conservation, waste management and sorting, and forest loss monitoring. The synthesis of recent research demonstrates that deep learning models (e.g., LSTMs, transformers, CNNs, XGBoost) often outperform traditional statistical methods in predicting spatiotemporal environmental patterns. Key case studies highlight significant achievements, such as reducing invasive species eradication costs by 81% using AI integrated camera traps, achieving 83% accuracy in waste classification with a two-stream neural network, and identifying deforestation drivers with an F1-score of 0.79 using the ForestNet model. However, the chapter also identifies critical limitations, including the failure of data-driven models to predict record-breaking climate extremes, the “black box” problem of model interpretability, class imbalance in environmental datasets, and sensor calibration issues. Based on this analysis, recommendations for future applications are provided, including the development of hybrid physics-AI models (e.g., NeuralGCM), the use of explainable AI (XAI) methods like SHAP, federated learning, edge computing, and the establishment of international ethical standards. The chapter concludes that while AI is a transformative tool for shifting environmental monitoring from reactive to proactive modes, its full potential requires interdisciplinary collaboration and careful handling of its inherent limitations.