Advanced hydrological modeling with machine learning and earth observations
| Vortragende/r (Mitwirkende/r) |
Ye Tuo [L]
|
|---|---|
| Nummer | 0000000938 |
| Art | Vorlesung mit integrierten Übungen |
| Umfang | 2 SWS |
| Semester | Sommersemester 2026 |
| Unterrichtssprache | English |
| Stellung in Studienplänen | Siehe TUMonline |
| Termine | Siehe TUMonline |
Teilnahmekriterien
Siehe TUMonline
Anmerkung: Elective module for the Master students in Environmental Engineering
Anmerkung: Elective module for the Master students in Environmental Engineering
Beschreibung
This module focuses on the practical joint applications of two important elements (Machine learning and Earth observation) in hydrological modeling domain. In step one, students start with an intensive Python tutorial to gain fundamental skills for further data processing and analysis and machine learning API adaptation and application. In step two, students learn practical skills to process earth observed hydrological data (such as precipitation, vegetation index products, surface temperature products, ET products, and soil moisture products) into required format with well-organized structure with programming. In step three, with the Earth Observation data from step two, the machine learning models including Linear Models, Support Vector Machines, Decision Trees, Ensemble models, and Neural network models will be taught. Regression, Classification, Clustering and Dimensionality reduction will be involved depending on the thematic topic of specific modeling tasks.
Inhaltliche Voraussetzungen
• Basics of programming in Python.
• Basics of GIS.
• Adequate knowledge of hydrology and hydrological modelling (relevant course records).
• Knowledge of Remote Sensing is an asset.
• Basics of GIS.
• Adequate knowledge of hydrology and hydrological modelling (relevant course records).
• Knowledge of Remote Sensing is an asset.
Lehr- und Lernmethoden
The module will be organized in an interactive manner as a lecture with integrated computational hand-on exercises. The lectures’ contents are presented by the lecturer using the digital slides and programming tutorials. During the exercises, the students independently solve practical examples and learn programming skills in Python. The students work in groups (max 3 persons per group) on one compulsory assignment, which tests the achievement of the study goals and competences based on more extensive case studies. A written report by each group is required to be submitted at the end.