Starting from 09:00 to 09:20

Graph neural networks for urban drainage systems metamodeling

• Speaker - Garzón Díaz

  Garzón Díaz

  ×

  Name

  Garzón Díaz

  Company

  TU Delft

  Country

  Netherlands

  
  Biographical Sketch
  
  Alexander Garzón is currently doing a PhD in the intersection of Artificial Intelligence and Urban Drainage. His work has been oriented towards the development of metamodels based on Graph Neural Networks for speeding up simulations of urban stormwater simulators.

  
  All Sessions of "Garzón Díaz"
  
  

  1. Graph neural networks for urban drainage systems metamodeling

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Starting from 09:20 to 09:40

Transferable Machine Learning methods for Predicting nitrate in diverse catchments

• Speaker - Janmohammadi

  Janmohammadi

  ×

  Name

  Janmohammadi

  Company

  Monash University

  Country

  Australia

  
  Biographical Sketch
  
  Mehran is a PhD candidate in Civil Engineering at Monash University. His work focuses on developing hard and soft sensors for measuring nutrients, specifically nitrate, in water systems.

  
  All Sessions of "Janmohammadi"
  
  

  1. Transferable Machine Learning methods for Predicting nitrate in diverse catchments
  2. A low-cost, in-situ, automated sensor for nitrate and ammonia

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Starting from 09:40 to 10:00

Prediction of urban stormwater quality in data-deficient areas using a semi-supervised machine learning framework

• Speaker - Yan

  Yan

  ×

  Name

  Yan

  Company

  University of Alberta

  Country

  Canada

  
  Biographical Sketch
  
  The author is a Ph.D. candidate at the University of Alberta, Department of Civil and Environment Engineering. He completed his Bachelor’s degree and Master’s degree in hydrology and water resources at Hohai University. His research interest lies in urban stormwater management, assessment of non-point source pollution, application and development of urban stormwater models, water resources planning and management.

  
  All Sessions of "Yan"
  
  

  1. Prediction of urban stormwater quality in data-deficient areas using a semi-supervised machine learning framework

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Starting from 10:20 to 10:25

Statistical and Machine Learning Models for Predicting Sediment Accumulation in an Urban River

• Speaker - Pimiento

  Pimiento

  ×

  Name

  Pimiento

  Company

  Universidade da Coruña

  Country

  Colombia

  
  Biographical Sketch
  
  Ph.D. Engineering, MSc. Hydrosystems, and BSc. civil Engineerin. My research interests and experiences are realted to Urban Drainage Management, especially in urban stormwater management.

  
  All Sessions of "Pimiento"
  
  

  1. 1.34 Machine Learning Predictive Modelling for Sediment Risk Indices within an Urbanized River Channel
  2. 2.18 Statistical and Machine Learning Models for Predicting Sediment Accumulation in an Urban River
  3. Environmental and human health risks from heavy metals in urban runoff sediments
  4. Statistical and Machine Learning Models for Predicting Sediment Accumulation in an Urban River
  5. Machine Learning Predictive Modelling for Sediment Risk Indices within an Urbanized River Channel

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