Starting from 09:00 to 09:20

Generative AI for Climate-adaptive Cities and Blue-Green Infrastructures: promises and perils in the future co-pilot?

• Speaker - Bach

  Bach

  ×

  Name

  Bach

  Company

  Eastern Switzerland University of Applied Sciences

  Country

  Switzerland

  
  Biographical Sketch
  
  Dr. Peter M. Bach is a senior research scientist at the Eastern Switzerland University of Applied Sciences (OST) and adjunct research fellow at Monash University, Australia. His research focus is on the planning of multi-functional Blue-Green Infrastructures (a.k.a. urban nature-based solutions) for climate-adaptive and liveable cities. He obtained his PhD in Water Systems Modelling in 2014 from Monash University and has since been expanding his research portfolio around Blue-Green Infrastructures and integrated modelling beyond the urban drainage sector, understanding its relationships with urban heat, biodiversity and liveability. He is also editor of Water Research X and member of the management committee of the IWA Specialist Group on Modelling and Integrated Assessment. Last, but not least, he also hosts his own podcast called "The Grand Challengers Podcast".

  
  All Sessions of "Bach"
  
  

  1. “Cool-Green D&D” - A digitalization and demonstration testbed for sponge city technology development and engagement
  2. Generative AI for Climate-adaptive Cities and Blue-Green Infrastructures: promises and perils in the future co-pilot?

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Starting from 09:20 to 09:40

Water Level Estimation in Sewage Pipes using Texture Based Methods and Machine Learning Algorithms

• Speaker - Bhase

  Bhase

  ×

  Name

  Bhase

  Company

  South West Water

  Country

  United Kingdom

  
  Biographical Sketch
  
  Kalyani is a dedicated data scientist at South West Water who is passionate about leveraging artificial intelligence in the field of water/sewage survey industry. She envisions a future where data analytics, machine learning and data driven insights play a pivotal role in optimizing and promoting overall efficiency of activities in the water industry.

  
  All Sessions of "Bhase"
  
  

  1. Water Level Estimation in Sewage Pipes using Texture Based Methods and Machine Learning Algorithms

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Starting from 09:40 to 10:00

Enhancing Combined Sewer Systems Modeling and Predictions through Physics-Leveraged Machine Learning

• Speaker - E. Bakhshipour

  E. Bakhshipour

  ×

  Name

  E. Bakhshipour

  Company

  RPTU in Kaiserslautern

  Country

  Germany

  
  Biographical Sketch
  
  Postdoctoral researcher at the RPTU in Kaiserslautern and co-founder of SWIS GmbH, a German-based startup that provides software solutions and consulting services for optimal planning, digitization, and automation in the water sector

  
  All Sessions of "E. Bakhshipour"
  
  

  1. Enhancing Combined Sewer Systems Modeling and Predictions through Physics-Leveraged Machine Learning

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Starting from 10:00 to 10:20

Data-Driven Machine Learning approaches for prediction of failure events due to impaired drainage asset condition

• Speaker - Wu

  Wu

  ×

  Name

  Wu

  Company

  University of Sheffield

  Country

  United Kingdom

  
  Biographical Sketch
  
  Dr. Yiqi Wu is a Post-doctoral Research Associate in Civil Engineering at the University of Sheffield. She has been collaborating closely with Network Rail on projects related to UK railway drainage assets. Yiqi has developed degradation and performance models that enable a better understanding of the impact of both asset conditions and future climate change on drainage performance. Her work extends to the development of a comprehensive whole-life cost framework, aiding in the effective management of assets by providing proactive intervention strategies to mitigate flooding risks and minimise overall life cycle costs. Additionally, Yiqi is actively involved in various research projects investigating the risk of railway drainage related failures using Machine Learning techniques.

  
  All Sessions of "Wu"
  
  

  1. 1.26 Adsorptive removal of Cu2+ and Zn2+ from highway runoff by using alum sludge in a Permeable Reactive Barrier System
  2. Analyzing the effectiveness of Blue and Green Infrastructures in urban environments using a fine-scale water balance model
  3. Adsorptive removal of Cu2+ and Zn2+ from highway runoff by using alum sludge in a Permeable Reactive Barrier System
  4. Coupling cross-scale modelling with SSP-RCP projections to investigate carbamazepine fates in rivers under climate change and urbanization
  5. Data-Driven Machine Learning approaches for prediction of failure events due to impaired drainage asset condition

  Close

Starting from 10:20 to 10:25

Digital Based Solution for the Reduction of Pollution sent to the Receiving Waters from Urban Drainage and Sanitation Systems: Madrid Case Study.

• Speaker - Lastra

  Lastra

  ×

  Name

  Lastra

  Company

  Canal de Isabel II

  Country

  Spain

  
  Biographical Sketch
  
  Civil Hydraulic Engineer. Innovation Development Area. R&D Department. (Canal de Isabel II. Madrid )

  
  All Sessions of "Lastra"
  
  

  1. 1.33 Digital Based Solution for the Reduction of Pollution sent to the Receiving Waters from Urban Drainage and Sanitation Systems: Madrid Case Study.
  2. 1.35 From Pathogens to Pharmaceuticals: Expansion of surveillance in wastewater. Madrid case of Study.
  3. From Pathogens to Pharmaceuticals: Expansion of surveillance in wastewater. Madrid case of Study.
  4. Digital Based Solution for the Reduction of Pollution sent to the Receiving Waters from Urban Drainage and Sanitation Systems: Madrid Case Study.

  Close

Starting from 10:25 to 10:30

Machine Learning Predictive Modelling for Sediment Risk Indices within an Urbanized River Channel

• 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

  Close