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Unchecked discharges of untreated water from combined sewer systems pose critical hazards to public health and ecosystems. To address this, regional sanitation authorities like the Metropolitan Sewer District of Greater Cincinnati (MSDGC) have begun collecting massive streams of hydrological telemetry, looking toward deep learning (DL) methodologies—specifically recurrent neural networks (RNN)—to forecast these spill events. Crucially, determining whether a DL model is truly suitable for this domain requires a structured analysis of the environment in which it operates. This paper introduces a requirements engineering methodology grounded in problem frames to map out and organize stakeholder concerns. This approach systematically evaluates real-world conditions where standard assumptions of high-quality data break down, translating these insights into actionable software testing benchmarks known as metamorphic relations, which govern both data input adjustments and output validations. Deploying this methodology within MSDGC’s operational context provides a rigorous, standardized baseline to assess how effectively competing RNN designs fulfill core operational mandates.