Protocols for Water and Environmental Modeling Using Machine Learning in California

The recent surge in popularity of generative artificial intelligence (GenAI) tools like ChatGPT has reignited global interest in AI, a technology with a well-established history spanning several decades. The California Department of Water Resources (DWR) has been at the forefront of this field, leveraging Artificial Neural Networks (ANNs), a core technique in machine learning (ML), which is a subfield of AI, for water and environmental modeling (WEM) since the early 1990s. While protocols for WEM exist in California, they were designed primarily for traditional statistical or process-based models that rely on predefined equations and physical principles. In contrast, ML models learn patterns from data and require different development methodologies, which existing protocols do not address. This study, drawing on DWR's extensive experience in ML, addresses this gap by developing standardized protocols for the development and implementation of ML models in WEM in California. The proposed protocols cover four key phases of ML development and implementation: (1) problem definition, ensuring clear objectives and contextual understanding; (2) data preparation, emphasizing standardized collection, quality control, and accessibility; (3) model development, advocating for a progression from simple models to hybrid and ensemble approaches while integrating domain knowledge for improved accuracy; and (4) model deployment, highlighting documentation, training, and open-source practices to enhance transparency and collaboration. A case study is provided to demonstrate the practical application of these protocols step by step. Once implemented, these protocols can help achieve standardization, quality assurance, interoperability, and transparency in water and environmental modeling using machine learning in California.