Road conditions analysis and forecasting in Arctic: multi-source machine learning approach ...
<!--!introduction!--> Climate change, global warming, and increasing weather extremes, especially in Sub-Arctic and Arctic regions with unusual freeze-thaw cycles, can cause more and more challenges to the infrastructure such as road networks. The maintenance and repair of road network can be...
| Main Authors: | , , , |
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| Format: | Conference Object |
| Language: | unknown |
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GFZ German Research Centre for Geosciences
2023
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| Online Access: | https://dx.doi.org/10.57757/iugg23-2849 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019034 |
| Summary: | <!--!introduction!--> Climate change, global warming, and increasing weather extremes, especially in Sub-Arctic and Arctic regions with unusual freeze-thaw cycles, can cause more and more challenges to the infrastructure such as road networks. The maintenance and repair of road network can be time consuming and expensive. Better targeted and proactively planned maintenance could have economical benefits and increase the safeness of the roads. To tackle this, artificial intelligence (AI) and machine learning (ML) techniques with the availability of digitalised diverse historical and real-time data, can be utilised, on one hand, to better understand the causes of the thaw damages and frost heave affecting the roads, and on the other hand, to build more advanced forecasting models for short- and long-term road conditions and thaw damage risks. In this work, as a first step, for building data-driven ML approaches to Arctic road damage forecasting, the possibilities of applying different multi-source are ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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