Learn how Arthian leveraged Flood Modeller's Python API to boost productivity and enhance client value
by David Hughes, Chartered Civil Engineer and Technical Lead for the Water Environment Team at Arthian
In keeping with my profession as a Chartered Civil Engineer at Arthian, I am always striving to improve workflows and enhance client deliverables. Flood Modeller’s Python API (application programming interface) has been a key part of this, helping to streamline processes, automate repetitive tasks, and focus more on analysis, design, and decision-making.
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Flood Modeller’s Python API stands out for its versatility, easy integration with other Python libraries, and the added benefit of being available at no additional cost. Its flexibility positions us to harness the power of artificial intelligence (AI) for advanced data analysis and pattern recognition, further enhancing the value we’re able to deliver to clients. From reviewing survey data to generating efficient outputs, the API has helped us achieve significant productivity gains in several key areas:
Automating Data Management
We have automated the review and formatting of topographic survey data, pulling it seamlessly into both new and existing network files, saving countless hours typically spent on manual data entry, and helping to ensure accuracy and consistency across a range of projects.
Enhancing Model Quality
We have developed scripts to automate a range of model health checks, such as validating and updating cross-section properties, panel markers, and conveyance curves, helping to increase the model build quality.
Automating Initial Conditions
We have partially automated the production of simulation initial conditions, which historically required significant manual effort.
Streamlining Sensitivity Testing
The setup of sensitivity testing has been partially automated, varying simulation parameters and results comparisons, allowing for faster and more thorough assessments and helping to communicate confidence in model outputs.
Improving Output and Results Analysis
Extraction, presentation, and communication of model results, as well as comparisons against external datasets in an accessible format for a range of technical and non-technical stakeholders, is now much more efficient.
These innovations have significantly reduced project timelines and freed up the Water Environment Team at Arthian for complex problem-solving tasks. The API’s adaptability and continued development ensure we can effectively focus on delivering exceptional value to our clients, whether identifying flood mitigation measures or optimising existing defences.
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As well as the API, what truly sets Flood Modeller apart is its support team. Their quick and knowledgeable responses have ensured minimal disruptions to our workflows whenever we encounter a challenge or need clarification.
In summary, Flood Modeller’s Python API has revolutionised our approach to flood risk management. By streamlining workflows, improving model quality, and enabling effective communication with diverse stakeholders, Flood Modeller has become an indispensable tool for the Water Environment Team at Arthian.
