Doctoral defence: Holger Virro “Geospatial data harmonization and machine learning for large-scale water quality modelling”
On 8 December at 14:15 Holger Virro will defend his doctoral thesis “Geospatial data harmonization and machine learning for large-scale water quality modelling” for obtaining the degree of Doctor of Philosophy (in Geoinformatics).
Associate Professor Evelyn Uuemaa, University of Tartu
Dr Alexander Kmoch, University of Tartu
Associate Professor Victor Francisco Rodriguez Galiano, University of Seville (Spain)
The state of freshwater quality continues to deteriorate worldwide due to agricultural pollution. In order to combat these issues effectively, water quality modeling could be used to better manage water resources. However, large-scale water quality models depend on input datasets with good spatial coverage.
The aim of the thesis was to improve and harmonize datasets for water quality modeling purposes and create a machine learning framework for national-scale modeling.
We created EstSoil-EH as a new numerical soil database for Estonia by converting the text-based soil properties in the Estonian Soil Map to machine-readable values. We used it to predict soil organic carbon content using the random forest machine learning method and found that the conditions of sampling locations affected prediction accuracy. We improved the global coverage of water quality data by producing the Global River Water Quality Archive (GRQA), which was compiled from five existing large-scale datasets. The compilation involved harmonizing the corresponding metadata, flagging outliers, calculating time series characteristics and detecting duplicate observations. We developed a framework suitable for national-scale water quality modeling based on lessons learnt from predicting soil carbon content. We used 82 environmental variables, including soil properties from EstSoil-EH as features to predict nutrient concentrations in 242 river catchments. The resulting models achieved accuracy comparable to the ones used previously in the Baltic region. We found that the size of the catchment influenced accuracy, since predictions were less accurate in smaller catchments. The models maintained reasonable accuracy even when the number of features was reduced by half, which shows that the relevance of features is more important than the amount. This flexibility makes our models applicable in areas that are otherwise lacking in the input data needed for extracting features.