Estimation of water quality parameters using remote sensing data and machine learning models
Event date: 2024-11-28
Event location: Scheelevägen 17, Lund
Welcome to this week’s Learning Machines seminar.
This seminar is a collaboration with CCAI Nordics – ccainordics.com.
Title: Estimation of water quality parameters using remote sensing data and machine learning models
Speaker: Alireza Taheri Dehkordi, Lund University
Abstract: The global decline in water quality, exacerbated by climate change and population growth, underscores the need for continuous and accurate monitoring of water quality parameters (WQPs). Remote sensing (RS) data, especially from multispectral satellites like Sentinel-2 and Landsat-8, offers large-scale, periodic observations for tracking WQPs. However, deriving accurate estimates solely from RS data is complex due to the intricate relationships between spectral bands and water quality indicators. This talk presents two novel machine learning approaches that leverage advanced RS data processing to enhance water quality monitoring.
About the speaker: Alireza is a doctoral student in Water Resources Engineering at Lund University, focusing on the combined applications of machine/deep learning, and remote sensing in hydrology. His primary interests lie in developing innovative AI-based models for groundwater level estimation using InSAR data and for water quality estimation through optical remote sensing. He is currently working on applying advanced models to extract more accurate, large-scale hydrological insights from these diverse data sources.
Location: Scheelevägen 17, Lund, or online using Zoom.
Date: 2024-11-28 15:00
This seminar will have an in-person presence at the RISE office in Lund, located at Scheelevägen 17, IDEON Science park, house Beta5, fourth floor. Make sure that you arrive in good time and knock at the door.
Upcoming seminars:
- 2024-12-12: Amal Nammouchi, Karlstad University and AfriClimate AI
- 2025-01-16: Oscar Täckström, Sana Labs
- 2025-01-23: Newton Mwai Kinyanjui, Chalmers University of Technology
- All seminars are 15:00 CET.
More information and coming seminars: https://ri.se/lm-sem
– The Learning Machines Team