Machine Learning for Post-Processing Hydrological Model Outputs
Day 2 Session 1 (10:30 AM)
Presenters:
Savalan Naser Neisary, The University of Alabama
The main goal of this workshop is to introduce a post-processing machine learning framework using the Long Short-Term Memory (LSTM) algorithm for hydrological model bias correction in operational settings, focusing on low flows and accounting for water operation impact. The session begins with an overview of machine learning concepts and the LSTM algorithm, followed by hands-on activities focusing on the ML model development pipeline, covering data preprocessing, feature selection, hyperparameter tuning, model training, and evaluation. By the end of the workshop, attendees will gain a solid understanding of ML techniques for hydrological applications, with practical experience in applying LSTM to improve the accuracy of hydrological models. This workshop is designed for hydrology professionals, researchers, and students, regardless of prior ML experience, and builds upon last year’s session with updated content and new insights.
Learning Outcomes:
- Understand the basics of machine learning and the Long Short-Term Memory (LSTM) algorithm.
- Gain practical skills in training and evaluating an LSTM model for hydrological applications.
- Learn to design and implement a post-processing framework for correcting biases in the National Water Model (NWM).
Prerequisites:
Knowledge:
- Basic knowledge of Python
- Basic knowledge of the National Water Model (NWM)
Hardware/Software:
- Laptop with browser installed to access CIROH 2i2c
Accounts: