dr.ir. A. Bomers | People Pages: Find employees & contact details

My scientific interests revolve around improving the computational efficiency of complex hydraulic river models. This involves two aspects, namely: improving the accuracy of hydraulic model predictions and decreasing computational times of highly-detailed hydraulic models. In my career, I focus on novel calibration methods and developing surrogate model techniques to simplify and replace our complex hydraulic river models.

During my PhD, I focussed on reconstructing historic flood events of the Rhine river using various surrogate modelling approaches. By reconstructing historic flood events, the data set of measured discharges starting in 1900 could be extended with almost 600 years. With this extended data set design discharges corresponding to large return periods can be predicted with less uncertainty. This is of high importance for desinging our flood defences.

In my role as an Assistant Professor, I aim to ultimately develop efficient data-driven surrogate models that can replace the highly detailed hydraulic models such that these type of models can be used as a real-time flood forecasting system. However, these data-driven models rely on the accuracy of the original hydraulic models. Therefore, I also aim to increase reliability of hydraulic models by improving current calibration methods. For more information, please also see: https://www.utwente.nl/en/et/research/sector-plan/sectorplan-stories/bomers/

Expertise

Earth and Planetary Sciences
- Flood
- Model
- River
- Event
- Investigation
- Time
- Dyke (Water management)
- Reconstruction

Organisations

Publications

2024

Increasing the water level accuracy in hydraulic river simulation by adapting mesh level elevation, Article 106135, 106135 (E-pub ahead of print/First online). Khorsandi kuhanestani, P., Bomers, A., Booij, M. J., Warmink, J. J. & Hulscher, S. J. M. H.https://doi.org/10.1016/j.envsoft.2024.106135Real time probabilistic inundation forecasts using a LSTM neural network, Article 131082. Hop, F. j., Linneman, R., Schnitzler, B., Bomers, A. & Booij, M. j.https://doi.org/10.1016/j.jhydrol.2024.131082Understanding and forecasting low flow river dune dynamics in a highly engineered lowland river. University of Twente. Lokin, L.https://doi.org/10.3990/1.9789036560870Improving mesh set-up increase accuracy of discharge capacity representation for water level prediction, 62-63. Khorsandi Kuhanestani, P., Bomers, A., Booij, M. J., Warmink, J. J. & Hulscher, S. J. M. H.https://cdn.bullit.digital/kbase/20240227205343/ncr-54-book_of_abstracts_ncrdays_2024_web.pdfFast computation of dike breach growth and outflow, 64-65. Besseling, L., Bomers, A., Warmink, J. J. & Hulscher, S. J. M. H.https://cdn.bullit.digital/kbase/20240227205343/ncr-54-book_of_abstracts_ncrdays_2024_web.pdfThe influence of vegetation occurrence on water levels, 110-111. Łoboda, A., Bomers, A. & Duong, T. M.https://cdn.bullit.digital/kbase/20240227205343/ncr-54-book_of_abstracts_ncrdays_2024_web.pdfUnderstanding the long-term dynamics of scour holes in lowland rivers, 86-87. Oldenhof, M., Bomers, A. & Hulscher, S. J. M. H.

2023

Neural networks for fast fluvial flood predictions: Too good to be true?, 1652-1658. Bomers, A. & Hulscher, S. J. M. H.https://doi.org/10.1002/rra.4144The effect of a local mesh refinement on hydraulic modelling of river meanders, 832-846. Bilgili, E., Bomers, A., Van lente, G. J., Huthoff, F. & Hulscher, S. J. M. H.https://doi.org/10.1002/rra.4110Is riverbank vegetation important for the estimation of flood water levels?In NCR DAYS 2023: Towards 2048: The next 25 years of river studies (pp. 110-111). Netherlands Centre for River Studies. Łoboda, A., Bomers, A. & Duong, T. M.Reconstruction of the 1374 Rhine river flood event around Cologne region using 1D-2D coupled hydraulic modelling approach, Article 129039. Ngo, H., Bomers, A., Augustijn, D. C. M., Ranasinghe, R., Filatova, T., van der Meulen, B., Herget, J. & Hulscher, S. J. M. H.https://doi.org/10.1016/j.jhydrol.2022.129039Application of machine learning for real-time prediction of dike breach inundation, 36-37. Besseling, L., Bomers, A. & Hulscher, S. J. M. H.https://cdn.bullit.digital/kbase/20230411204005/ncr-51-book_of_abstracts_ncrdays_2023_web.pdfImproving mesh set-up to increase discharge capcity accuracy for water level prediction, 54-55. Khorsandi Kuhanestani, P., Bomers, A., Booij, M. J., Warmink, J. J. & Hulscher, S. J. M. H.https://cdn.bullit.digital/kbase/20230411204005/ncr-51-book_of_abstracts_ncrdays_2023_web.pdfThe impact of riverbank vegetation coverage on rising water levels. Łoboda, A., Bomers, A. & Duong, T. M.Towards real-time probabilistic assessment of dike failure and corresponding outflow. Besseling, L., Bomers, A. & Hulscher, S. J. M. H.Improving mesh set-up to increase cross-sectional-area accuracy for water-level prediction. Khorsandi Kuhanestani, P., Bomers, A., Booij, M. J., Warmink, J. J. & Hulscher, S. J. M. H.

2022

Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network, Article 105. Kilsdonk, R. A. H., Bomers, A. & Wijnberg, K. M.https://doi.org/10.3390/hydrology9060105River Dune Dynamics During Low Flows, Article e2021GL097127. Lokin, L. R., Warmink, J. J., Bomers, A. & Hulscher, S. J. M. H.https://doi.org/10.1029/2021GL097127Inventing a hydraulic river modelling approach to simulate high flow and low flow conditions, 58-59. Khorsandi Kuhanestani, P., Bomers, A., Booij, M. J., Warmink, J. J. & Hulscher, S. J. M. H.Assessing the performance of an LSTM neural network and a HAND model for long-term real-time flood forecasting, 24-25. Besseling, L. & Bomers, A.

Research profiles

Courses Bachelor Civil Engineering:

Design project Civil Engineering, Bsc level, 2016-2021
Tutor and mentor
Minor smart cities: Multifunctional flood defences, Bsc level, 2021
Supervision of project groups
International study tour, Bsc/Msc level, 2022
Grading assignments and the 3-weeks study tour in Dubai and South-Korea

Courses Master Civil Engineering and Management:

Hydraulic modelling, Msc level, 5 EC.
Set up of course: coordination, arranging guest lectures, designing exercises, assignments, examination and grading.
River Dynamics, Msc level, 7.5 EC, 2017-2019
Supervising the tutorials
Design Project Water, MSc level, 7.5 EC, 2015-2019
Responsible for modelling part of the course

Supervision of BSc graduation project:

P. van Diggelen, 2024: Accelerating a detailed 1D2D hydrodynamic model
J. Belaiyneh, 2024: Hydrological validation of groundwater levels in groundwater monitoring networks
K. Korporaal, 2022: The influence of input parameters on flood calculation outcomes
R. den Hertog, 2021: De Chaamse Beken in Tygron
J. Bod, 2021: Spreading of the runoff times in the Dutch Rhine delta
S.A. de Vreeze, 2018: Hoogwaterverwachtingen op de Overijsselse Vecht
J. Willink, 2017: Process change analysis at waterboard Rijn & IJssel
R. de Boer, 2016: The effect of retaining water extremes in nature areas on water policy

Supervision of MSc graduation project:

E.G. Beelen, 2024: The influence of lateral inflow on the water level in the IJssel
J.B. Mooijaart, 2023: Assessment of 1D and 2D model choices on model accuracy and computation time in D-Hydro
F.J. Hop, 2023: Rapid generation of probabilistic inundation forecasts by utilizing cloud computing and deep learning
O.S. Bakker, 2022: Spatial planning & flood risk: development of a spatial planning framework for the mitigation of flood risk
L.S. Besseling, 2022: Dike breach prediction of an LSTM compared to the HAND.FLOW model for real-time flood forecasting
N. Klein Wolterink, 2022: Smart combinations: an alternative to dike reinforcements?
M. Geurts, 2022: Numerical analysis of flow characteristics near neighbouring vegetation patches of different densities
L. Leummens, 2021: Sensitivity of channel-size estimations on flood inundations
M. Flohr, 2021: Improvement of rainfall-runoff simulations on urban unpaved surfaces
S.E. Overmeen, 2021: Determining the wind drag coefficient in hydrodynamic modelling of a shallow, fetch-limited water system
R.A.H. Kilsdonk, 2021: Predicting flooding due to extreme precipitation in an urban environment using machine learning algorithms
E. Bilgili, 2020: The influence of a grid structure on hydraulic river modelling outcomes of river meanders
R. Dierx, 2020: Modelling breach erosion of cover sand ridges in the IJssel floodplain induced by water overflow in early medieval times
D. Booij, 2020: Morphodynamic modelling of migrating mid-channel bars in rivers using dynamic vegetation. A case study of the Ayeyarwady River
J.J.M. Thissen, 2019: Automating surface water detection for rivers
Y. Fredrix, 2018: Exploring the use of surrogate models to reconstruct historic discharges

Affiliated study programs

Courses academic year 2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2022/2023

FutureFRMtech: Understanding the behaviour of river scour holes

2023-now: Supervisor of M. Oldenhof (PhD project). Scour holes can threaten the stability of structures such as bridge piers, pipelines and dike foundations. Therefore, scour holes are often artificially filled after formation while this disturbs the positive effects on natural river bed dynamics and habitat restoration. This research aims to provide a better understanding of the long-term evolution of scour holes in a heterogeneous subsurface

Machine learning and conceptual modelling approaches for fast flood inundation predictions

2022-now: Supervisor of L.S. Besseling (PhD project). A quick overview of the potential flooded areas is required after a dike breach to enable evacuation of the areas at risk on time. This project focusses on developing conceptuel and machine learning approaches to decrease computational times of demanding numerical models currently used to predict flood inundations.

Adaptive calibration approaches for hydraulic river models to link high flow and low flow conditions

2021-now: Supervisor of Parisa Khorsandi Kuhanestani (PhD project). Much research has been done on the calibration of maximum water levels during flood events to design flood mitigation measures. However, up till now, low flow conditions did not have much research interest while it may have serious economic consequences if water levels in a river are below the navigable depth. The development of river dunes are one of the main drivers of the river water levels during low flow. Therefore, this study tries to set up a novel calibration method such that both high and low flow conditions can be simulated accurately by improving, among others, the description of the main channel bed roughness based on the physical properties of river dunes.

River dune dynamics under high and low flows

2020-now: Co-supervisor of Lieke R. Lokin (PhD project). River dunes are dynamic periodic bedforms at the riverbed, which are formed by flow over a movable riverbed. These river dunes are present in all alluvial rivers, such as the Rhine, the Mississippi and the Amazon river. During flood waves these river dunes grow in length and height and during low water levels they decay. Currently we do not know how the growth and decay processes of river dunes work exactly and therefore we are unable to predict their development. Understanding the processes that play a role in the evolution of these river dunes and being able to model them, helps river managers to predict bed form dynamics. It helps them to plan dredging measures before dunes become obstacles during low water levels. As during low water, the highest dune determines the navigable depth. This research aims to better understand and model the dynamics of river dunes under variable flow conditions, to predict river dune dynamics several days to weeks ahead.

Floods of the past - Design for the future

Design standards for flood protection in deltas require magnitude estimates of extreme (millennial) floods. The Dutch Delta Programme considers a design discharge of 18,000 m3/s an appropriate upper value the Rhine River at the German-Dutch border. Absence of a sufficiently long observational record of river discharge introduces considerable uncertainty in estimates of magnitude-frequency relations, which can only partly be solved by using statistical methods. To solve this problem, this project focusses on the extension of the data set of observed discharges by reconstructing historic flood events using novel hydraulic modelling approaches.

2020-2023: Daily supervisor of Hieu Ngo (PostDoc project). This project focusses on the reconstruction of the 1374 flood event of the Rhine river representing the largest flood of the last 1,000 years. Furthermore, this project evaluates the social impacts of such a large flood event in present times based on sophisticated Agent-Based modelling. The project is a collaboration between the MFS and BMS groups of the University of Twente, Utrecht University and IHE Delft.
2015-2020: PhD candidate. I established efficient modelling approaches to be able to reconstruct historic flood events. Both the suitability of lower-fidelity physically based surrogate models as data driven surrogate models have been evaluated. This project was finalised by extending the data set of measured discharges starting in 1900 with approximately 600 years. This project is a collaboration between the University of Twente and Utrecht University.

Large Wood Hydraulic (LaWoHy)

2019-2022: Researcher. This project focusses on the effect of large wood in rivers on the flow patterns and turbulence characteristics. The project is a collaboration between Vienna Technical University (TU Wien) and the University of Twente. TU Wien performs flume experiments delivering valuable measurements regarding changes in flow velocities and turbulent kinetic energies caused by the obstacles in the flow. It is my task to model these flume experiments using the opensource numerical software OpenFoam. Various turbulence models are compared to help practitioners waith making appropriate modelling choices.

Others

Elected member of the faculty council
Organizer of the NCR-days 2021 conference, Enschede
Reviewer for e.g. the following journals: Nature, Computers & Geosciences (CAGEO), Water, journal of Hydrology, Ecohydrology, Journal of Flood Risk Management