EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Geomorphological hazards assessment using machine learning and data fusion

Mohammad Ahmed1, Hamed Farhadi2, Panagiotis Michalis1, and Manousos Valyrakis1
Mohammad Ahmed et al.
  • 1University of Glasgow , School of Engineering , Civil engineering , United Kingdom of Great Britain and Northern Ireland (
  • 2Ferdowsi university of Mashhad, Water Science and Engineering Department, Iran

Turbulent flows may destabilise riverbeds and banks, transporting sediment or underscouring hydraulic infrastructure built near water bodies. For example, scour is a significant challenge that can affect the stability of bridge foundations as the transport of sediment around a bridge pier may cause structural instabilities and catastrophic failures. The aim of this study is to use machine learning techniques & data driven algorithms to predict how energetic turbulent flow events can result in the removal of individual sediment grains, resting on the bed surface or on the protective armour layer around built infrastructure. 

The flume experiments involve flow and particle motion data gathering campaigns [1]. Turbulent flow data are collected upstream the exposed target particle using acoustic Doppler velocimetry. Particle's motion data are gathered using novel micro-electro-mechanical sensors embedded within its waterproof casing, for a range of flow conditions. The obtained data are fed into neural networks having distinct algorithmic complexity (inputs, levels and neutrons). A comparison of the performance of the various model architectures, as well as with past ones [2], is conducted to identify the optimal predictive algorithm for the configuration tested. Sensor data fusion combined with artificial intelligence techniques are shown to provide a unique tool for live and robust data-driven predictions to help tackle significant engineering problems, such as geomorphological activity and scouring of infrastructure (eg bridge piers and embankments) due to turbulent flows, which become increasingly more challenging, under the scope of climate change and intensifying extreme weather hazards.



[1] Valyrakis, M., Farhadi, H. 2017. Investigating coarse sediment particles transport using PTV and “smart-pebbles” instrumented with inertial sensors, EGU General Assembly 2017, Vienna, Austria, 23-28 April 2017, id. 9980.

[2] Valyrakis, M., Diplas, P., Dancey, C.L. 2011b. Prediction of coarse particle movement with adaptive neuro-fuzzy inference systems, Hydrological Processes, 25 (22). pp. 3513-3524. ISSN 0885-6087, doi:10.1002/hyp.8228.

How to cite: Ahmed, M., Farhadi, H., Michalis, P., and Valyrakis, M.: Geomorphological hazards assessment using machine learning and data fusion , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20770,, 2020