Feasibility study on the use of machine learning methods for a semi-automated age structure assessment of the BQE fish in flowing waters according to the guideline for the survey of biological quality elements - A1 Fische-FW
Abstract
The aim of the project is to develop an improved methodology for the semi-automated age structure assessment of the BQE fish in flowing waters in accordance with Guideline A1 on the basis of fish surveys in flowing waters and machine learning processes. Data sets from more than 4,000 surveys from the "national water status monitoring" and other data from projects that can be used to assess the ecological status of fish are currently available throughout Austria. A core element of the assessment, which is otherwise automated, is the "assessment of the length-frequency diagrams" (= assessment of the age structure), which must be carried out on the basis of an expert assessment and therefore also represents a weak point. Experienced colleagues have tried to automate this step, but have so far only been successful in some areas. As part of the project, methods from the machine learning (ML) field, such as "XGBoost" or "Random Forests", are now to be used and adapted to carry out an assessment of the length-frequency diagrams based on fish caught and measured in a section.
keywords Machine Learning Hydrology Fish-Age Distribution
Publikationen
Project staff
Karsten Schulz
Univ.Prof. Dipl.Geoökol. Dr.rer.nat. Karsten Schulz
karsten.schulz@boku.ac.at
Tel: +43 1 47654-81699
Project Leader
01.12.2023 - 30.11.2024
Bano Mehdi-Schulz
Ass.Prof. Bano Mehdi-Schulz MSc. Ph.D.
bano.mehdi@boku.ac.at
Tel: +43 1 47654-81619
Sub Projectleader
01.12.2023 - 30.11.2024