Subsurface Topographic Modeling Using Geospatial and Data Driven Algorithm

Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability and resilience often are poorly recognized. In the current paper a 3D topographical predictive model using...

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Published in:ISPRS international journal of geo-information Vol. 10; no. 5; p. 341
Main Authors: Abbaszadeh Shahri, Abbas, Kheiri, Ali, Hamzeh, Aliakbar
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2021
Subjects:
Algorithms
Bedrock
Chromosomes
Concrete dams
Dams
Economic activities
Gene expression
Gene mapping
Genetic algorithms
Geology
geospatial
geostatistical
geostatistics
GEP
Hydrogeology
infrastructure
Intelligence
kriging
Mutation
Performance indices
Precipitation
prediction
Prediction models
predictive model
Rockfill dams
Soils
Spatial data
Spatial variations
Stability
Statistical methods
subsurface
Sustainability
Three dimensional models
topography
Urbanization
Iran
ISSN:2220-9964, 2220-9964
Online Access:Get full text
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Abstract Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability and resilience often are poorly recognized. In the current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed and applied on a concrete-face rockfill dam (CFRD) in Guilan province- northern to generate spatial variation of the subsurface bedrock topography. The compared proficiency of the GEP model with geostatistical ordinary kriging (OK) using different analytical indexes showed 82.53% accuracy performance and 9.61% improvement in precisely labeled data. The achievements imply that the retrieved GEP model efficiently can provide accurate enough prediction and consequently meliorate the visualization insights linking the natural and engineering concerns. Accordingly, the generated subsurface bedrock model dedicates great information on stability of structures and hydrogeological properties, thus adopting appropriate foundations.
AbstractList Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability and resilience often are poorly recognized. In the current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed and applied on a concrete-face rockfill dam (CFRD) in Guilan province- northern to generate spatial variation of the subsurface bedrock topography. The compared proficiency of the GEP model with geostatistical ordinary kriging (OK) using different analytical indexes showed 82.53% accuracy performance and 9.61% improvement in precisely labeled data. The achievements imply that the retrieved GEP model efficiently can provide accurate enough prediction and consequently meliorate the visualization insights linking the natural and engineering concerns. Accordingly, the generated subsurface bedrock model dedicates great information on stability of structures and hydrogeological properties, thus adopting appropriate foundations.
Author Hamzeh, Aliakbar
Abbaszadeh Shahri, Abbas
Kheiri, Ali
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SubjectTerms Algorithms
Bedrock
Chromosomes
Concrete dams
Dams
Economic activities
Gene expression
Gene mapping
Genetic algorithms
Geology
geospatial
geostatistical
geostatistics
GEP
Hydrogeology
infrastructure
Intelligence
kriging
Mutation
Performance indices
Precipitation
prediction
Prediction models
predictive model
Rockfill dams
Soils
Spatial data
Spatial variations
Stability
Statistical methods
subsurface
Sustainability
Three dimensional models
topography
Urbanization
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