The river Rur is an important river for the Limburg Water authority. But a unique combination of storage capacity and precipitation issues, and complex dependencies shared with neighbouring German water authorities, mean that its flows are very difficult to model through traditional hydrologic approaches.
To help overcome that challenge, the water authority asked RHDHV Digital to develop a new peak flow prediction model, based on a combination of data science and machine learning techniques, and its expert hydrological knowledge.
The water authority wanted to be able to accurately forecast peak flow levels up to 24 hours into the future. That meant the model needed large volumes of reliable data on peak flow events in order to be trained effectively.
While there was data dating back to the early 2000s, the data from 2000 to 2010 contained several incomplete time series. So, the decision was made to train the model using our most complete event data sets – those created from 2010 onwards.
We created a linear regression prediction model that combines historic discharge, precipitation, and precipitation surplus data with knowledge about interactions between drought and precipitation from 2010 and onwards.
To ensure the relationships and patterns considered by the machine learning model deliver reliable output, we thoroughly analysed how the area of the river functioned from a hydrological perspective and selected data based on this analyses. This enabled us to formulate valuable prediction features, and assure reliability when they were implemented.
We utilised a Scrum methodology to execute this project, enabling the client to stay actively involved in the process and use their hydrology expertise to ensure that all data being used to train the model was of high quality.
Working closely with the client team, we defined, reviewed and refined four sprints for the project:
This resulted in a model that can accurately forecast the peak flow of the river Rur near Stah at least 16 hours in advance, at a 90 percent confidence interval.
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