Novel application of Google earth engine interpolation algorithm for the development of geotechnical soil maps: a case study of mega-district

Ijaz, Zain; Zhao, Cong; Ijaz, Nauman; Rehman, Zia ur; Ijaz, Aashan

doi:10.1080/10106049.2022.2138566

Cited by 16 publications

(9 citation statements)

References 45 publications

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“…Equation ( 2) presents that the mean value of the local average random field x h (z) is the same as that of the original random field x(z). Equation (3) shows that the spatial mean-variance under space mean conditions is reduced by a certain degree based on the point variance.…”

Section: Calculation Methods Of Correlation Distancementioning

confidence: 99%

“…Pile foundation stability mainly depends on the physical and mechanical properties of the foundation soil [1]. Studies have shown that physical and mechanical parameters of each point in the foundation soil of a certain thickness have strong spatial variability, which is determined by the material composition and structural characteristics of soil, and the spatial variability of foundation soil parameters has a great influence on the safety and stability of pile foundation and project cost [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Correlation Distance of Soil Parameters on Pile Foundation Failure Probability

et al. 2023

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Spatial variability of soil parameter distribution is crucial to calculating the pile foundation failure probability. Traditional reliability design methods describe the dispersion degree of soil parameters with their point variance without considering the influence of correlation distance. In this paper, static cone penetration test data of a project site are used, and random field theory is introduced to describe the average spatial characteristics of soil parameters. Then, the method of spatial average is used to calculate the correlation distance of soil parameters in each foundation soil layer. Given the influence of the correlation distance, a variance reduction function is determined to convert point variance to spatial mean-variance and further calculate the failure probability of pile foundation with the Monte Carlo method to study the influence of correlation distance on pile foundation failure probability. Results show that the spatial variability of parameters can be better reflected, and project cost can be reduced by considering the influence of correlation distance during the pile foundation design process. These results lay a foundation for further research on the pile foundation reliability design method.

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Section: Calculation Methods Of Correlation Distancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Correlation Distance of Soil Parameters on Pile Foundation Failure Probability

et al. 2023

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“…Meanwhile, in addition to traditional algorithms such as inverse distance weighting (IDW) and Kriging on geographic information system (GIS) platforms, advancements in big data and improved algorithms integrated with the Google Earth Engine (GEE) platform have shown promise in accurately predicting the geotechnical/geological parameters [4]. Particularly, many studies in climatology, environmental science, agriculture, chemistry, hydrology, mineralogy, and soil science have used the GEE platform in conjunction with advanced spatial interpolation techniques, such as IDW, Kriging, Spline, and Co-kriging [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Advanced geospatial modeling and statistical evaluation of heterogenous geotechnical facets at multiple depths using the improved formulation of modified Shepard IDW algorithm

Ijaz,

Zhao,

Ijaz

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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A formidable challenge in geology and geotechnics is the significant spatial heterogeneity in the subsoil characteristics in fine-scale grids. To mitigate this problem, geotechnical data is integrated as geotechnical soil maps (GSMs), which uses sophisticated interpolation techniques and provides an advanced understanding and accurate depiction of subsurface variability. This study uses an improved formulation of inverse distance weighting (IDW) algorithm based on modified Shepard method, integrated with the Google Earth Engine platform. The prediction efficiencies of GSMs and traditional IDW algorithm are statistically evaluated and compared, considering heterogeneous geotechnical facets at multiple depths in an unexplored region. Pertinent geotechnical properties including soil type, plasticity index, and standard penetration test were considered to evaluate the algorithm performance based on critical performance metrics. The results demonstrate that the improved formulation of the IDW algorithm is more relevant to field values and tends to align with Tobler's first law of geography by inducing a smooth transition rather than a disruptive trend owing to high geotechnical variability. The prediction accuracy increased by 10 – 20% compared to the traditional IDW algorithm. This study demonstrates and promotes the use of an improved formulation of the modified IDW algorithm considering its better accuracy and relevance to field value

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“…To surmount this challenge, the efficacy of digital mapping of soil salinity must depend on the combined use of multiple environmental covariates [6]. Zain et al [7] evaluated several research areas according to key geotechnical characteristics in the Lahore metropolitan area based on IDW interpolation technology, which is based on the improved Shephard method that can efficiently generate very accurate geotechnical engineering soil maps. Ijaz et al [8] used spatial interpolation technology to create a spatial map (SM) in the Sialkot area based on a large amount of geotechnical foundation data, and used linear regression analysis to establish the correlation, so that soil strength, stiffness, and soil consistency can be quickly and reliably evaluated.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China

Du,

He,

Luo

et al. 2024

Sustainability

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In order to investigate the mechanism of environmental factors in soil salinization, this study focused on analyzing the temporal-spatial variation of soil salinity in the Ogan-Kuqa River Oasis in Xinjiang, China. The research aimed to predict soil salinity using a combination of satellite data, environmental covariates, and advanced modeling techniques. Firstly, Boruta and ReliefF algorithms were employed to select variables that significantly affect soil salinity from the Sentinel-2 satellite data and environmental covariates. Subsequently, a soil salinity inversion model was established using three advanced strategies: comprehensive variable analysis, a Boruta-based variable selection algorithm, and a ReliefF-based variable selection algorithm. Each strategy was modeled using a Light Gradient Boosting Machine (LightGBM), an Extreme Learning Machine (ELM), and a Support Vector Machine (SVM). Finally, the Boruta-LightGBM strategy was proven to be the most effective in predicting soil electrical conductivity (EC), with a coefficient of determination (R2) of 0.72 and a Root Mean Square Error (RMSE) of 12.49 ds/m. The experimental results show that the red-edge band index is the foremost variable in predicting soil salinity, succeeded by the salinity index and soil attribute data, while the topographic index has the least influence, which further demonstrates that proper variable selection could significantly improve model functionality and predictive precision. Furthermore, the Multiscale Geographically Weighted Regression (MGWR) model was utilized to reveal the influence and temporal-temporal-spatial heterogeneity of environmental factors such as soil organic carbon (SOC), precipitation (PRE), pH value, and temperature (TEM) on soil EC. This research offers not just a viable methodological framework for monitoring soil salinization but also new perspectives on the environmental drivers of soil salinity changes, which have implications for sustainable land management and provide valuable information for decision-making in soil salinity control and mitigation efforts.

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Novel application of Google earth engine interpolation algorithm for the development of geotechnical soil maps: a case study of mega-district

Cited by 16 publications

References 45 publications

Influence of Correlation Distance of Soil Parameters on Pile Foundation Failure Probability

Influence of Correlation Distance of Soil Parameters on Pile Foundation Failure Probability

Advanced geospatial modeling and statistical evaluation of heterogenous geotechnical facets at multiple depths using the improved formulation of modified Shepard IDW algorithm

Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China

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