A Novel Combination Co-Kriging Model Based on Gaussian Random Process

Xie, Huan; Zeng, Wei; Song, Hong; Sun, Wen; Ren, Tao

doi:10.1155/2018/6372572

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Based on visual inspection and quantitative appraisal both for interpolated values and standard errors, it is suggested that CK (with soil cohesion as covariate) performs better than IDW and OK in estimating spatial distribution of soil depth to hardpan in Western Central Java. This is consistent with the aim of Cokriging model development (Myers 1984) and its implementation 5 (Minnitt and Deutsch 2014;Adhikary et al 2017;Xie et al 2018).…”

Section: Interpolation Performancesupporting

confidence: 82%

Initial Assessment of Landslide Prone Area using Soil Properties

Yanto

Apriyono

Santoso

et al. 2019

Preprint

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Initial assessment of landslide prone area is important in designing landslide mitigation measures. This study, a part 5 of our study on developing landslide spatial model, presents initial signal of landslide prone area. In here, we use soil depth to hardpan to assess landslide prone area in Western Central Java, a relatively small region where 23% of Indonesian landslide occurs. To this end, we interpolated soil depth to hardpan in a regular grid from irregularly distributed data. To do this, we employed three different methods: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Co-Kriging (CK). For the latter, we experimented with several potential covariates. To determine the best fitting model, several tests on model 10 performance and its corresponding errors were done. Error measures used in this study are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), while statistical measures employed are Standard Deviation, Variance, Interquartile Range (IQR), Mean Absolute Deviation and MedianAbsolute Deviation. The result shows that CK with covariate of slope and soil cohesion is the best fitting model and exhibits clear pattern related to recorded landslide disaster sites. We found that 64% of landslide disaster events occur in the area having 15 soil depth to hardpan of 5 -10 m. Moreover, 84% of landslide occurrences happen in regions where soil depth to hardpan ranges from 5 to 15 m. Hence, we suggest that landslide prone area is an area possessing soil depth to hardpan of 5-15 m. This finding is advantageous for policy makers in planning and designing efforts for landslide mitigation. IntroductionIndonesia has a large number of landslide disaster occurrences. In last decade, 3,924 landslide disaster events were recorded 20 with 1,404 events (~36%) of them occurred in the Central Java province (BNPB 2018). Out of those numbers, 908 landslide events occurred in western part of the area. In this region, landslide disaster events have caused 211 casualties, nearly 20,000 people suffered and more than 7,000 house damaged. Moreover, the number of natural disaster in Indonesia shows increasing trend from 924 events in 2008 to 2,862 events in 2017 which accounts for more than threefold during the last decade (BNPB 2018). Considering the impact of global climate change, the number could be larger and the impact could be worsening 25 (Banholzer et al. 2014). Hence, appropriate mitigation measures in particular locations are critical. Accordingly, understanding landslide prone area is helpful in designing policy for appropriate mitigation efforts.Landslide occurs when safety factor (SF), defined as a ratio of shear strength and acting force (gravitational force, seepage pressure, etc) of soil layer disrupted (Das 1998). The higher the slope stability (SF > 1) the lower the possibility of landslide

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Section: Interpolation Performancesupporting

confidence: 82%

Initial Assessment of Landslide Prone Area using Soil Properties

Yanto

Apriyono

Santoso

et al. 2019

Preprint

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“…Kriging has many advantages; such as being a proper approach to multi-dimensional problems and its prediction capability as compared to other metamodeling techniques [31][32] and being able to produce error estimates. On the other hand, Kriging technique is still developing by many research groups from all around the world [33][34][35][36][67][68]. Those development studies are rather concentrate on the sampling types [37], on the tuning parameter exploration such as variogram adaptation [38] or on adding some descriptive new information into the algorithm such as gradient/hessian enhanced types [39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Surrogate Based Aft Form Optimization of Ships Using High Fidelity Solvers

Solak

2020

brod

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Surrogate (metamodel) based optimization has numerous potential applications in the field of naval architecture. It is aimed here to establish a methodology for the aft form optimization for minimum viscous resistance, thus the present study is focused on the aft form where the viscous effects become dominant. It is necessary to solve this problem within acceptable time span from practical naval architectural point of view which requires metamodeling techniques currently under investigation. Accordingly, the present paper investigates the metamodeling ability of the Kriging interpolation and attempts to explore its capabilities and limitations in the aft form optimization from viscous resistance point of view. As metamodeling techniques become more widely used, their constraints are more apparent. Especially in highly nonlinear design spaces, the effect of dimensionality should be taken into consideration. Taking all those factors into account, the present paper is to examine the capabilities of Kriging and to establish the learning performance in terms of RMS error, correlation coefficient and required number of training points according to selected optimization algorithm for multidimensional ship design problem. The results show that, at least 5% reduction in viscous pressure drag can be attained by the present optimization methodology.

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