Discovering Effective Policies for Land-Use Planning with Neuroevolution
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866918026011475968 |
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| author | Young, Daniel Francon, Olivier Meyerson, Elliot Schwingshackl, Clemens Bieker, Jacob Cunha, Hugo Hodjat, Babak Miikkulainen, Risto |
| author_facet | Young, Daniel Francon, Olivier Meyerson, Elliot Schwingshackl, Clemens Bieker, Jacob Cunha, Hugo Hodjat, Babak Miikkulainen, Risto |
| contents | How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and removals, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset LUH2 and the bookkeeping model BLUE. It generates Pareto fronts that trade off carbon impact and amount of land-use change customized to different locations, thus providing a proof-of-concept tool that is potentially useful for land-use planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12304 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Discovering Effective Policies for Land-Use Planning with Neuroevolution Young, Daniel Francon, Olivier Meyerson, Elliot Schwingshackl, Clemens Bieker, Jacob Cunha, Hugo Hodjat, Babak Miikkulainen, Risto Neural and Evolutionary Computing Artificial Intelligence Machine Learning How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and removals, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset LUH2 and the bookkeeping model BLUE. It generates Pareto fronts that trade off carbon impact and amount of land-use change customized to different locations, thus providing a proof-of-concept tool that is potentially useful for land-use planning. |
| title | Discovering Effective Policies for Land-Use Planning with Neuroevolution |
| topic | Neural and Evolutionary Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2311.12304 |