Effect of Normalization Methods on Accuracy of Estimating Low- and High-Molecular Weight PAHs Distribution in the Soils of a Coking Plant

Yuan, Yumin; Yang, Kai; Cheng, Li–Rong Lilly; Bai, Yijuan; Wang, Yingying; Ying, Hanjie; Ding, Aizhong

doi:10.3390/ijerph192315470

Cited by 4 publications

(7 citation statements)

References 33 publications

(43 reference statements)

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“…The quality of the comparison of results between several estimations can be assessed using estimation quality statistics. These include the following [8,38,40,41…”

Section: Validation Methodsmentioning

confidence: 99%

“…For deterministic methods, only ME and RMSE can be calculated. Optimally, the values of ME and MSE should be as close to zero as possible [8,40,42,43]. RMSE is useful for comparing interpolation results from different methods; it should be as small as possible.…”

Section: Validation Methodsmentioning

confidence: 99%

“…If the ASE is greater than the RMSE, then this indicates that the variability in the dataset has been overestimated; on the contrary, it means that the variability has been underestimated. As for the RMSSE, it should reach a value close to 1 [8,42,43].…”

Section: Validation Methodsmentioning

confidence: 99%

“…This minimization of error variation distinguishes Kriging from other methods. Equation (8) shows an estimation of unknown values at different points using different sets of weights [34]:…”

Section: Krigingmentioning

confidence: 99%

“…The most popular geostatistical interpolation method is called Kriging. It is regarded as an optimal method of spatial prediction that weighs the surrounding measured points to calculate a prediction for an unknown location [8,9]. It is widely used for digital spatial data modeling, as well as bathymetrical data modeling in many variants, e.g., in [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Selected Geostatistical Methods for Bottom Surface Modeling

Biernacik

Kazimierski

Wlodarczyk–Sielicka³

2023

Sensors

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Digital bottom models are commonly used in many fields of human activity, such as navigation, harbor and offshore technologies, or environmental studies. In many cases, they are the basis for further analysis. They are prepared based on bathymetric measurements, which in many cases have the form of large datasets. Therefore, various interpolation methods are used for calculating these models. In this paper, we present the analysis in which we compared selected methods for bottom surface modeling with a particular focus on geostatistical methods. The aim was to compare five variants of Kriging and three deterministic methods. The research was performed with real data acquired with the use of an autonomous surface vehicle. The collected bathymetric data were reduced (from about 5 million points to about 500 points) and analyzed. A ranking approach was proposed to perform a complex and comprehensive analysis integrating typically used error statistics—mean absolute error, standard deviation and root mean square error. This approach allowed the inclusion of various views on methods of assessment while integrating various metrics and factors. The results show that geostatistical methods perform very well. The best results were achieved with the modifications of classical Kriging methods, which are disjunctive Kriging and empirical Bayesian Kriging. For these two methods, good statistics were calculated compared to other methods (for example, the mean absolute error for disjunctive Kriging was 0.23 m, while for universal Kriging and simple Kriging, it was 0.26 m and 0.25 m, respectively). However, it is worth mentioning that interpolation based on radial basis function in some cases is comparable to Kriging in its performance. The proposed ranking approach was proven to be useful and can be utilized in the future for choosing and comparing DBMs, mostly in mapping and analyzing seabed changes, for example in dredging operations. The research will be used during the implementation of the new multidimensional and multitemporal coastal zone monitoring system using autonomous, unmanned floating platforms. The prototype of this system is at the design stage and is expected to be implemented.

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“…The quality of the comparison of results between several estimations can be assessed using estimation quality statistics. These include the following [8,38,40,41…”

Section: Validation Methodsmentioning

confidence: 99%

Section: Validation Methodsmentioning

confidence: 99%

Section: Validation Methodsmentioning

confidence: 99%

“…This minimization of error variation distinguishes Kriging from other methods. Equation (8) shows an estimation of unknown values at different points using different sets of weights [34]:…”

Section: Krigingmentioning

confidence: 99%