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 land-use change


Discovering Effective Policies for Land-Use Planning

Miikkulainen, Risto, Francon, Olivier, Young, Daniel, Meyerson, Elliot, Bieker, Jacob, Cunha, Hugo, Hodjat, Babak

arXiv.org Artificial Intelligence

How areas of land are allocated for different uses, such as forests, urban, and agriculture, has a large effect on carbon balance, and therefore climate change. Based on available historical data on changes in land use and a simulation of carbon emissions/absorption, 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 and the BLUE simulator. It generates Pareto fronts that trade off carbon impact and amount of change customized to different locations, thus providing a potentially useful tool for land-use planning.


Mapping the world's fungal networks with machine learning - Geographical Magazine

#artificialintelligence

Life is underpinned by fungi. Fungal filaments extend through the soil in networks of mycelia, but we know relatively little about them and questions abound, including where on Earth they are and in what diversity and abundance. Vast and powerful, mycelial networks sequester carbon, hold soils together and supply as much as 80 per cent of all nutrients to terrestrial plants. In just one hectare of grassland, the extent of fungi is equivalent to around 12 million times the length of the Amazon River. But just like the distributions of plant species, those of fungi are almost certainly shifting in response to climate change.