Geospatial modeling of heterogeneous geotechnical data using conventional and enhanced conception of modified Shepard method-based IDW algorithms: application and appraisal

Characterization and accurate assessment of subsurface soil information from geological and geotechnical perspectives are the major challenges considering urban planning, site selections, and designing preemptive response systems. In this regard, this article deals with the application and appraisal of inverse distance weighting (IDW) interpolation algorithms for the characterization and development of geotechnical maps (GMs) using a conventional Geographic Information System (GIS) and state-of-the-art Google Earth Engine (GEE) platform. Unlike GIS, the modified GEE-based IDW algorithm operates on the enhanced conception of the modified Shepard method that adds artificial diffusion and smoothens the prediction surface. Contrarily, GIS-based IDW outcomes steep prediction with compromised accuracy. Besides, GMs performance utilizing GIS- and GEE-based IDW algorithms based on key performance indices (KPIs) is yet to be defined. Soil type, standard penetration test (SPT-N), activity (A-value), and soil consistency are used to develop GMs, which are major geotechnical parameters for foundation design. GIS- and GEE-based IDW interpolation algorithm-based GMs demonstrate distinct variations in the areal coverage of geotechnical categories across the region, ranging between 3 and 5%. GEE-based GMs showed a smooth transition of prediction values. Based on KPIs, GEE-based GMs showed 10–15% less error than GIS and correlated better with field values. Conclusively, the GEE-based IDW interpolation algorithm is more effective in reducing error and exhibits a strong correlation with field values. The study’s findings highlighted the adoption of the enhanced conception of a modified IDW algorithm using the GEE platform due to its better prediction.