Machine learning and geospatial modeling in hydrology
Day 3 Session 1 (10:30 AM)
Presenters:
Jonathan Frame, University of Alabama
Machine learning/Deep learning methods for geospatial modeling in hydrologic sciences. Students will utilize convolution networks for predictions of hydrological conditions from images. We will utilize synthetic datasets to learn methodology. We will utilize satellite images to apply methods to real-world scenarios.
Learning Outcomes:
- Hands on experience training and deploying geospatial machine learning model for hydrological research
Prerequisites:
Knowledge:
- Experience training machine learning models of any sort.
- Will largely build on last year’s demonstration of time series modeling with machine learning.
- Will include both temporal and spatial prediction from previous modeling demonstration.
Hardware/Software:
- Computer with several GB of storage and 1 GPU or an Apple M-series processor
- Git
- Data from HydroShare
Accounts: