Statistical analysis of the best GIS interpolation method for bearing capacity estimation in An-Najaf City, Iraq

Abstract

The presence of an economical solution to predict soil behaviour is essential for new construction areas. This paper aims to investigate the ultimate interpolation method for predicting the soil bearing capacity of An-Najaf city-Iraq based on field investigation information. Firstly, the engineering bearing capacity was calculated based on the in-site N-SPT values using dynamic loading for 464 boreholes with depths of 0–2 m, using the Meyerhof formula. The data then were classified and imported to the GIS program to apply the interpolation methods. Four deterministic and two geostatistical interpolation methods were applied to produce six bearing capacity maps. The statistical analyses were performed using two methods: the common cross-validation method by the coefficient of determination (R2) and root mean square error (RMSE), where the results showed that ordinary kriging (OK) is the ultimate method with the least RMSE and highest R2. These results were confusing so, the backward elimination regression (BER) procedure was applied to gain the definite result. The results of BER show that among all the deterministic methods, the IDW is the optimal and most significant interpolation method. The result of geostatistical methods shows that EBK is the best method in our case than the OK method. BER also applied to all six methods and shows that IDW is the ultimate significant method. The results indicate no general ultimate interpolation method for all cases and datasets type; therefore, the statistical analyses must be performed for each case and dataset.

Qualitative Model of the Causal Interactions between Phytoplankton, Zooplankton, and Environmental Factors in the Romanian Black Sea

“…Spatial distribution maps were produced with ArcGIS Desktop 10.7 [45]. GIS modeling tools are employed for spatial interpolation, a technique that predicts the values of unsampled points or data gaps within a study area [46]. Using this method proves particularly valuable in examining extensive marine environments, where constraints such as time and financial resources hinder comprehensive data collection across entire study areas [47].…”

“…Spatial interpolation involves predicting the value of a variable at a location where it has not been directly measured based on data collected at known locations. In our study, we utilized local interpolators, specifically inverse distance weighting (IDW) [46,47]. Zooplankton samples were collected by employing vertical tows, with a Juday net (0.1 m 2 mouth opening area, 150 µm mesh size) equipped with a flow meter used to estimate the filtered water volume [36].…”

The Spatio-Temporal Distribution Characteristics of Carbon Dioxide Derived from the Trajectory Mapping of Ground Observation Network Data in Shanxi Province, One of China’s Largest Emission Regions

“…The mapped values of the comparative station are extracted from the obtained dataset and then compared with the seasonal averages of the in situ information. The evaluation is conducted by using R, RMSE and Bias [18], as shown in Figure 2.…”

“…The mapped values of the comparative station are extracted from the obtained dataset and then compared with the seasonal averages of the in situ information. The evaluation is conducted by using R, RMSE and Bias [18], as shown in Figure 2. By comparing Rall, Rdown and Rtop among the six stations, it is evident that the Rdown is the largest at Datong, Wutaishan, Taiyuan and Jincheng stations, which are all significant at the 0.001 level.…”

Spatial Distribution and Evaluation of Heavy Metal Pollution in Soil Based on Three-Dimensional Model and Kriging Interpolation

“…For geostatistical methods, the most widely used is the ordinary Kriging method developed from the mineral reserves (Coulibaly et al, 2021;Park et al, 2019). The ordinary Kriging method is a geostatistical interpolation method based on spatial correlation variance and is used to find the best linear unbiased estimation (Al-Mamoori et al, 2021;Belkhiri et al, 2020;Hasan et al, 2021). The formula is as follows:…”