Unveiling the illusion of successful water quality governance using deep learning

Observable improvements in surface water quality are often interpreted as evidence of effective governance. However, such conclusions may be misleading when hydrological variability and socio-economic activities obscure inconsistencies in policy implementation. This study develops a deep learning prediction-error framework that combines Convolutional Neural Networks and Long Short-Term Memory networks to predict multiple water quality indicators across China’s Pearl River Basin and to retrospectively assess governance performance. By comparing predicted and observed water quality, the approach identifies temporal and spatial patterns where regulatory signals are strong or weak. The analysis reveals that prediction errors serve as sensitive markers of governance inconsistency, particularly in economically underdeveloped regions where seemingly good water quality does not necessarily reflect robust pollution control. Occasional anomalies, such as short-term degradation coinciding with major public holidays, are presented as examples of governance-related temporal irregularities detectable through this method. Overall, the results demonstrate that deep learning models can serve not only as predictive tools but also as diagnostic instruments for uncovering hidden governance issues, offering a more nuanced evaluation of environmental management than water quality observations alone.