https://github.com/martibosch/invest-ucm-calibration

Automated calibration of the InVEST urban cooling model with simulated annealing
https://github.com/martibosch/invest-ucm-calibration

calibration natural-capital simulated-annealing urban-heat-islands

Last synced: 17 days ago
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Automated calibration of the InVEST urban cooling model with simulated annealing

• Host: GitHub
• URL: https://github.com/martibosch/invest-ucm-calibration
• Owner: martibosch
• License: gpl-3.0
• Created: 2020-05-06T15:53:00.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2024-01-31T10:22:36.000Z (about 1 year ago)
• Last Synced: 2025-03-24T03:05:12.909Z (about 1 month ago)
• Topics: calibration, natural-capital, simulated-annealing, urban-heat-islands
• Language: Python
• Homepage: https://doi.org/10.5194/gmd-14-3521-2021
• Size: 261 KB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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# InVEST urban cooling model calibration

## Overview

Automated calibration of the InVEST urban cooling model with simulated annealing

**Citation**: Bosch, M., Locatelli, M., Hamel, P., Remme, R. P., Chenal, J., and Joost, S. 2021. "A spatially-explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)". *Geoscientific Model Development 14(6), 3521-3537*. [10.5194/gmd-14-3521-2021](https://doi.org/10.5194/gmd-14-3521-2021)

See [the user guide](https://invest-ucm-calibration.readthedocs.io/en/latest/usage.html) for more information, or [the `lausanne-heat-islands` repository](https://github.com/martibosch/lausanne-heat-islands) for an example use of this library in an academic article.

## Installation

The easiest way to install this library is using conda (or mamba), as in:

```bash

conda install -c conda-forge invest-ucm-calibration

```

which will install all the required dependencies including [InVEST](https://github.com/conda-forge/natcap.invest-feedstock) (minimum version 3.11.0). Otherwise, you can install the library with pip provided that all the dependencies (including GDAL) are installed.

## 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)

- Support spatio-temporal datasets with [xarray](http://xarray.pydata.org) 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

## Acknowledgments

- The calibration procedure is based simulated annealing implementation of [perrygeo/simanneal](https://github.com/perrygeo/simanneal)

- With the support of the École Polytechnique Fédérale de Lausanne (EPFL)

- This package was created with the [ppw](https://zillionare.github.io/python-project-wizard) tool. For more information, please visit the [project page](https://zillionare.github.io/python-project-wizard/).