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 resilience often poorly recognized. In current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed applied on concrete-face rockfill dam (CFRD) Guilan province- northern generate spatial variation bedrock topography. The compared proficiency GEP geostatistical ordinary kriging (OK) different analytical indexes showed 82.53% accuracy performance 9.61% improvement precisely labeled data. achievements imply that retrieved efficiently can provide enough prediction consequently meliorate visualization insights linking natural engineering concerns. Accordingly, generated dedicates great information stability structures hydrogeological properties, thus adopting appropriate foundations.