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Heuristics‐enhanced geospatial machine learning (SaaS) of an ancient Mediterranean environment
Journal article   Open access   Peer reviewed

Heuristics‐enhanced geospatial machine learning (SaaS) of an ancient Mediterranean environment

K.B.W. Svatos, Z. Ahmed, A. Recalde‐Salas and T. Young
Soil Science Society of America Journal, Vol.86(3), pp.604-611
2022
DOI: https://doi.org/10.1002/saj2.20385
Appears in  Open Access via Read & Publish Agreements
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CC BY-NC-ND V4.0,  Open Access

Abstract

Raw soil core physical data used in machine learning algorithms with corresponding spatial remotely sensed data is an emerging science. Using data derived from soil core samples previously collected in Universal Transverse Mercator zone 50 (Western Australia) and remotely sensed data, a model that predicted ground movement (GM) was developed specific to Australian Standards manual AS 1726–2017. This is the first approach for Australian soils and first in the world for soils older than 200 million yr. The model developed reliably predicted GM with 91.1% accuracy. The error obtained from the prediction is within acceptable limits currently used by engineers in calculations concerning soil classification for engineering purposes. Concerning the remotely sensed data analyzed, accuracy of the Atterberg limits method might be improved if additional information about soil structure (layering and horizon) or other variables (seasonal data) are built into this model. This model can be used to save on construction material costs, reduce the potential for human error associated with data collection and sample manipulation, but also fast-track (by up to 6 wk based on current wait times) building approvals while ensuring compliance to the relevant legislation. This platform also reduces the environmental effects of invasive drilling techniques. A requirement within principles of sustainable building practices, and associated with current standards commonly used by structural engineers who may seek better understanding of soil properties in Australia as a software service (with application potential in North America).

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Collaboration types
Domestic collaboration
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7 Engineering & Materials Science
7.133 Geotechnical Engineering
7.133.986 Unsaturated Soil Mechanics
Web Of Science research areas
Soil Science
ESI research areas
Agricultural Sciences
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