Data supporting the study: Estimating Urban Interception Evaporation Using a Multi-source Data-driven Model at a Flux Site
Overview This repository contains the data and code supporting the study "Estimating Urban Interception Evaporation Using a Multi-source Data-driven Model at a Flux Site". The dataset includes processed observational data, geospatial datasets, and machine learnin
Overview
This repository contains the data and code supporting the study
“Estimating Urban Interception Evaporation Using a Multi-source Data-driven Model at a Flux Site”.
The dataset includes processed observational data, geospatial datasets,
and machine learning model outputs used for model training, validation, and analysis.
Contents
– Processed flux observations used for model training and validation (.RData)
– `/gis/`
– Shapefiles (.shp) describing land cover and spatial features
– GeoTIFF files (.tif) representing spatial raster inputs
– `/code/`
– R scripts for data processing, model training, and analysis
– `/model/`
– Trained machine learning model outputs and prediction results
Processing and methods
Raw data were quality-controlled, aggregated, and transformed prior to analysis.
Machine learning models were trained using the processed datasets.
Model hyperparameters and training procedures are documented in the provided code.
Detailed methodological descriptions are provided in the associated manuscript.
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Files are hosted on the source repository. Click download to access the full dataset.