AI News, Storm damage to forests costs billions – here’s how artificial intelligence can help

Storm damage to forests costs billions – here’s how artificial intelligence can help

High-intensity storms cause billions of pounds of damage every year, and climate change is set to make this worse in future.

Gales force Forestry is an important contributor to the UK economy, with an annual gross value of around £2bn – slightly more than 0.1 per centof the total economy.

This area is increasing all the time, both to meet the rising demand for timber and for environmental reasons.In England, the recently announced Northern Forest between Hull in the east and Liverpool in the west will help with flood prevention, soil loss and protection of wildlife.

The forestry industry attempts to reduce the risk of wind damage in various ways, including harvesting trees at a younger ageand thinning forests earlier to increase the long-term stability of the trees.

It relies on a type of artificial evolution called genetic programming, which mimics evolution in the natural world to come up with completely new features that can be fed into a classification system to make it easier to discriminate between different trees.

The new approach also provides new insights to forestry managers, for example highlighting the factors that most influence susceptibility to damage – such as tree density – which in turn helps them to develop better forest management plans for the future.

And the models work sufficiently fast that the impact of these management plans can be mapped in real time, which is extremely helpful for forest planning and engaging with stakeholders.

We don’t know of anyone else trying to apply machine learning to forest risk management, but there are parallels in numerous areas – breast cancer diagnosis, to give one example.

Storm damage to forests costs billions – here’s how artificial intelligence can help

High-intensity storms cause billions of pounds of damage every year, and climate change is set to make this worse in future.

Gales force Forestry is an important contributor to the UK economy, with an annual gross value of around £2bn – slightly more than 0.1 per centof the total economy.

This area is increasing all the time, both to meet the rising demand for timber and for environmental reasons.In England, the recently announced Northern Forest between Hull in the east and Liverpool in the west will help with flood prevention, soil loss and protection of wildlife.

The forestry industry attempts to reduce the risk of wind damage in various ways, including harvesting trees at a younger ageand thinning forests earlier to increase the long-term stability of the trees.

It relies on a type of artificial evolution called genetic programming, which mimics evolution in the natural world to come up with completely new features that can be fed into a classification system to make it easier to discriminate between different trees.

The new approach also provides new insights to forestry managers, for example highlighting the factors that most influence susceptibility to damage – such as tree density – which in turn helps them to develop better forest management plans for the future.

And the models work sufficiently fast that the impact of these management plans can be mapped in real time, which is extremely helpful for forest planning and engaging with stakeholders.

We don’t know of anyone else trying to apply machine learning to forest risk management, but there are parallels in numerous areas – breast cancer diagnosis, to give one example.

Storm damage to forests costs billions — here's how artificial intelligence can help

High-intensity storms cause billions of pounds of damage every year, and climate change is set to make this worse in future.

This area is increasing all the time, both to meet the rising demand for timber and for environmental reasons: in England, the recently announced Northern Forest between Hull in the east and Liverpool in the west will help with flood prevention, soil loss and wildlife.

forestry industry attempts to reduce the risk of wind damage in various ways, including harvesting trees at a younger age and thinning forests earlier to increase the long-term stability of the trees.

commonly use a software system called ForestGALES to help estimate the probability of wind damage to groups of trees — stands as they are called in the industry.

Modeling techniques (PDF) also exist to predict storm damage to individual trees, based on things such as their height, width and more general forest characteristics such as soil type.

It relies on a type of artificial evolution called genetic programming (GP), which mimics evolution in the natural world to come up with new features that can be fed into a classification system to make it easier to discriminate between different trees.

The new approach also provides new insights to forestry managers, for example highlighting the factors that most influence susceptibility to damage — such as tree density — which in turn helps them to develop better forest management plans for the future.

And the models work sufficiently fast that the impact of these management plans can be mapped in real time, which is extremely helpful for forest planning and engaging with stakeholders.

We don’t know of anyone else trying to apply machine learning to forest risk management, but there are parallels in numerous areas — breast cancer diagnosis, to give one example.

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