Exploring the Potential of Deep Learning for Efficient Assessment of Water System Vulnerabilities
Climate stress testing is a well-established methodology for assessing the long-term vulnerabilities of water
systems under uncertain climate conditions. This involves simulating the sensitivities of hydrology and water
allocation models to changes in relevant climate statistics using plausible climate change scenarios. However, the
high computational requirements of this process have limited its use primarily to academic studies.
In this research proposal, we aim to explore the potential of using deep learning techniques to reduce the
computational burden and speed up climate stress testing experiments. While data-driven approaches have been
increasingly used in hydrological modeling, their application to climate change studies poses a new challenge due
to the inclusion of new conditions that are not present in the historical data used to train the models.
Our research aims to determine whether this approach can effectively reduce the computational burden of climate
stress testing and make this methodology more accessible for practical applications beyond academic studies.
Enrolled in a relevant MSc program software development, data science, earth observation,
or innovative technologies for modeling or monitoring.
To facilitate collaboration with Dutch universities for our internship program, we encourage students who are currently enrolled in a Dutch university and residing in the Netherlands to apply.
Program duration is 6 – 9 months starting September 2023.
Interning with Deltares means a monthly student allowance of €550 (depending on your
study points), flexible working hours, and the chance to work alongside top researchers from a Triple A institute.
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