1. Your Model is not Purpose-Driven
The first mistake is embarking on a project without a clear purpose. With an increasing amount of available network data, many modelers are too concerned with building hydraulic models that are expansive, more so than they are useful.
2. Low-Confidence in the Data
If inaccurate - or incomplete data - informs your model output, then your model will not accurately present or predict network performance. Often, system data exists in multiple places such as in GIS, CMMS, Excel, or manually recorded field notes. Your model should leverage the most up-to-date and verified information from your network.
3. Insufficient Investment
Investing the proper budget and time into developing your hydraulic model is critical to make sure your model solves real-world challenges and that your return on investment is quickly realized.
4. Not Cross-Referencing Results with Observed Data
If you receive a compelling output from a model simulation – how does that translate to what is observed in the field? Check observed data to make sure what you simulate is close to how the system is truly operating.
5. Infrequent Calibration and Verification
A useful model needs to be frequently calibrated, and the simulation outputs should be rectified against observed network data. A model calibrated every 5 years will not reflect the most current network conditions and will not be as informative as it could be.
6. Feature Unfamiliarity
If a model is not delivering useful insights, you may need to refresh familiarity with the full features and capabilities available in your modeling software. This will ensure your team’s experience is up to speed with what you need from your model.
7. Working in Organizational Silos
As with any working project, being collaborative and open to bringing in cross-functional expertise can lead to a better outcome.
8. Not Understanding the Full Hydraulic Competency of Your Network
Storage tanks, pump stations, combined sewer outfalls and regulators, and other components can increase the complexity of a wastewater network. Make sure you understand how these hydraulic structures impact each other to ensure they are accurately modeled.
9. Impractical Scenario Evaluation
A purpose-driven model needs to evaluate scenarios that are truly applicable to the daily operation of a network. These need to be aligned with financial, ecological, and community goals set by your organization. If a simulation is not feasible according to the model’s purpose, resources should not be devoted to it.
10. Relying too Much on Previous Model Data
Using historical data is not always the best way to develop an updated hydraulic model. Historic environmental and physical data may no longer be representative of current network performance. Even if the data comes from a previous model, always reference recently recorded data for better accuracy