‘HAL24K combines advanced data science techniques – such as machine learning and deep neural networks – with modelling, analysis, and visualisation, to enable real-time data-driven decision making in complex and multidimensional environments. This optimises resources, avoids disruptions and saves costs.
For example, we built a model for Rijkswaterstaat to analyse water quality measurements in the Waal. Rijkswaterstaat is part of the Dutch Ministry of Infrastructure and Water Management and as such is responsible for the design, construction, management and maintenance of the main infrastructure in the Netherlands. The measurements are taken weekly at the point at which the river enters the Netherlands, and monthly further downstream. We were able to demonstrate that it is possible to predict the water quality downstream based on these weekly measurements. In addition, we found strong correlations between some of the 100 parameters measured thereby showing that artificial intelligence can reduce the number of parameters measured. This makes the process more efficient and economical while not compromising on quality.
Rijkswaterstaat also provides daily information on the location of the shallowest depths in rivers in the Netherlands so that vessels can navigate safely. Inspectors measure the river depths by heading out to the traditionally shallowest parts. Our data scientists proved that by quickly processing a lot of complex data from multiple sources, we can design predictive, objective real-time models which describe the behaviour of the river bottom across the 100 km stretch of the River Waal and predict the shallowest parts each day. We are currently starting a follow-on project to the Rijkswaterstaat’s ‘The Digital River’ programme to predict the shallowest parts up to several days in advance. Machine learning and the data generated will improve the models’ performance.
Part of day-to-day business
HAL24K’s objective is always that operations directly and continuously benefit clients and other stakeholders. We want to make sure that our work helps people manage systems better and more efficiently every day. Our solutions must therefore be scalable. Data science is not hocus pocus and it is important that we take AI and machine learning out of the lab and into day-to-day business. Without implementation, there is no innovation.