Physics Maths Engineering
Subsurface Topographic Modeling Using Geospatial and Data Driven Algorithm
Abbas Abbaszadeh Shahri,
Ali Kheiri,
Aliakbar Hamzeh
Peer Reviewed
Related Subjects
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.
1. What role do infrastructures play in urbanization and economic activities?
Infrastructures are fundamental to urban growth and economic operations. However, they are often vulnerable, and the lack of accurate subsurface mapping undermines the sustainability and resilience of these structures.
2. What was the aim of the study presented in the paper?
The study aimed to develop a 3D topographical predictive model using distributed geospatial data and evolutionary gene expression programming (GEP) to model subsurface bedrock topography at a concrete-face rockfill dam (CFRD) in Guilan province.
3. How did the GEP model compare to traditional geostatistical methods?
The GEP model showed 82.53% accuracy, outperforming the traditional ordinary kriging (OK) method, which had an R² of 0.92, with a 9.61% improvement in prediction performance.
4. What is the significance of the generated subsurface model?
The subsurface model offers valuable insights into the stability of structures and hydrogeological properties, which are crucial for determining appropriate foundation designs and improving the safety of infrastructure.
