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Personal profile

Scholarly biography

Georgios is a Lecturer of Physical Geography in the School of Environment and Technology. He has a 5-year Diploma in Environmental Engineering (Technical university of Crete, Greece), a MSc in Freshwater Systems Science (University of Glasgow) and a PhD in Geography and Computing Sciences (University of Glasgow). 

His PhD work embedded advancing Micro Electrical Mechanical Sensor (MEMS) technologies into the monitoring of sediment motion and combined several mathematical and electrical/mechanical engineering techniques during sensor development. He has held the position of Research Associate in a NERC funded cooperation between Scottish Water (the largest water supplier in Scotland) and the University of Glasgow. He has also worked as Senior Hydromophologist for the Scottish Environmental Protection Agency (SEPA). 

Georgios has developed a track-record of peer reviewed publications addressing fluvial hydraulics, sediment movement and purpose specific sensor development. He has also worked in various aspects of Geomorphic Change Detection and is particularly interested in comparing data from different sensing techniques across scales. He has extensive fieldwork experience and during his placement in SEPA he provided scientific input in diverse regulatory, river management and river restoration projects. In 2017, he received one of the Early Career Researcher Grants from the British Society of Geomorphology (Autumn round).

Approach to teaching

Like every aspect of our lives, the teaching process needs to keep up with a historical transformation: we are moving from a world of lack of information to a world that there is too much of it.  I have extensive experience in teaching geographical techniques (eg. Statistics and GIS analysis) and my approach is underlined by the requirement for 'upgrading' information to some type of knowledge. I like re-thinking fundamental concepts, presenting the science before the tools and engaging in real and open research discussions.

There are ideas that take time to understand regardless how hard someone studies them. This is why context from real world problems and fieldwork should be a key part of teaching, especially for topics relating to applied disciplines like Physical Geography or Environmental Engineering.

Research interests

My interests lie on the intersection between coarse grain sediment transport, reach scale river dynamics, advanced sensor development, advanced statistical and numerical modelling of multiphase environmental flows and data coherence analysis for geomorphological applications. There are three problems on which I am focusing my efforts at the moment:

Smart pebbles and what we can learn from them (in geomorphology)

During the last decade, many scientists developed and deployed ‘’smart- pebbles’’ in fluvial (and other rapidly changing environments) in an attempt to monitor sediment dynamics. In parallel, Inertial Measurements Units (IMUs) have been tested in laboratory experiments focusing mainly on fluvial single grain entrainments and sort-term motions (simulating either costal or river hydrodynamics). Although all the IMUs are in principle the same (an assembly of micro-accelerometer, micro-gyroscope and micro-compass), the parameters that affect the results range from the sensor’s electrical and physical characteristics to the filtering of the derived measurements and from the modelling of inertial kinematics to the transformation of those to a useful and informative piece of data. I try to undestand a) the key error sources in IMU sensing and its realistic range of applicability, b) how to develop coherent error compensation strategies for taking measurments in natural environments c) how smart pebbles can inform the theoretical descriptions for fluvial sediment transport d) how we can upscale this information to enhance our risk assesments for the critical infrastructure exposed to geomorphic hazards.

Advanced topographic sensing and Geomorphic Change Detection

We experience a revolution in terms of how we acquire and analyse topographical data. The integration of GIS with advanced sensing equipment has made the modelling of landscapes easier than ever before. One of the best examples is the deployment of Unmanned Aerial Vehicles (UAVs) for mapping, a technique that has increased the rate and decreased dramatically the cost of creating accurate topographical models. However, UAVs (but also other innovative techniques) come with a number of limitations that become more apparent when we attempt to compare different maps of the same area (over time) in order to quantify geomorphic change (Geomorphic Change Detection). The mapping becomes even more complicated when fluvial environments are investigated. I am interested in the margin of error that we have to account for when using those techniques and how that associates with the modelling/mapping of reach scale river processes.  

Coherence in river classification

Researchers and regulators often use a classification in order to distinguish between different river typologies or assign a quality/health score to a river environment. The methodologies behind this scoring vary significantly from purely qualitative to highly technical and quantitative. I am interested in quantifying the variability in the interpretation of those classifications. More specifically, I want to measure how sensitive are these scores to user bias, the type/complexity of the associated calculations and the complexity of the natural environment scored using Deep Learning techniques.


Education/Academic qualification

PhD, University of Glasgow

Master, University of Glasgow

Bachelor, Technical University of Crete

External positions

Honorary Research Associate, University of Glasgow

1 Mar 2018 → …


  • GB Physical geography
  • Fluvial Geomorphology
  • Sediment transport
  • Sensors
  • Statistical modelling
  • Resilience

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

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sensor Earth & Environmental Sciences
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Research Output 2013 2019

Smart pebbles go hiking: in-situ measurements of grain scale dynamics using inertial sensors in mountain streams

Maniatis, G., Apr 2019.

Research output: Contribution to conferenceAbstractResearchpeer-review

Open Access
mountain stream
in situ measurement

How close are we to the perfect smart pebble?

Maniatis, G., Apr 2018, p. 8804-8804. 1 p.

Research output: Contribution to conferenceAbstractResearchpeer-review

sediment transport
coastal sediment

A walk by the river: three-dimensional reconstruction of surface sedimentology and topography using wearable laser scanning

Williams, R., Lamy, M. L., Stott, E. & Maniatis, G., Dec 2017.

Research output: Contribution to conferenceAbstractResearchpeer-review

grain size

Bank stability as a risk factor for pipeline infrastructure: a Scottish example

Maniatis, G., Williams, R. & Hoey, T. B., 2017.

Research output: Contribution to conferenceAbstractResearchpeer-review

Open Access
bank erosion
risk factor

Quantifying the morphodynamics of river restoration schemes using Unmanned Aerial Vehicles (UAVs)

Williams, R., Byrne, P., Gilles, E., Hart, J., Hoey, T. B., Maniatis, G., Hamish, M., Reid, H. & Ves, N., 2017.

Research output: Contribution to conferenceAbstractResearchpeer-review

Open Access
spatial distribution
decision making

Activities 2013 2018

  • 2 Invited talk
  • 2 Visiting an external academic institution

WSL. Swiss Federal Institute for Forest, Snow and Landscape Research

George Maniatis (Visiting researcher)

Activity: Visiting positionVisiting an external academic institution

IMU sensors and the reality of geomorphological research

George Maniatis (Presenter)

Activity: External talk or presentationInvited talk

University of British Columbia

George Maniatis (Visiting researcher)
Sep 2013Dec 2013

Activity: Visiting positionVisiting an external academic institution