Automated calibration of the InVEST urban cooling model with simulated annealing
Project description
InVEST urban cooling model calibration
Overview
Automated calibration of the InVEST urban cooling model with simulated annealing
See the user guide for more information.
Installation
This library requires specific versions of the gdal
and rtree
libraries, which can easily be installed with conda as in:
$ conda install -c conda-forge 'gdal<3.0' rtree 'shapely<1.7.0'
Then, this library can be installed as in:
$ pip install invest-ucm-calibration
An alternative for the last step is to clone the repository and install it as in:
$ git clone https://github.com/martibosch/invest-ucm-calibration.git
$ python setup.py install
TODO
- Allow a sequence of LULC rasters (although this would require an explicit mapping of each LULC/evapotranspiration/temperature raster or station measurement to a specific date)
- Test calibration based on
cc_method='intensity'
- Support spatio-temporal datasets with xarray to avoid passing many separate rasters (and map each raster to a date more consistently)
- Read both station measurements and station locations as a single geo-data frame
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