Strengthening Machine Learning Reproducibility to Ensure Water Security in the Long Term

Water security is increasingly threatened by rapid climate change, population growth, and inadequate water management. While machine learning (ML) offers promising solutions for water resource allocation, flood forecasting, and water quality monitoring, its long-term effectiveness is hindered by reproducibility challenges. This chapter emphasizes the urgent need to improve ML reproducibility in the water sector by addressing issues such as poor data documentation, lack of shared algorithms, inconsistent hyperparameter tuning, and absence of standardized evaluation frameworks. These issues are especially critical in complex, region-specific water systems where small variations can lead to major predictive discrepancies. Enhancing reproducibility requires transparent data practices, open-access model sharing, standardized performance metrics, and cross-disciplinary collaboration. The integration of explainable AI (XAI) and the development of reproducibility as a core evaluation criterion are crucial. Strengthening ML reproducibility will ensure reliable, sustainable deployment of predictive tools to support long-term water security under escalating environmental and societal pressures.