Comparison of the Kriging and neural network methods for modeling foF2 maps over North China region

Jiang, Chunhua; Zhou, Chen; Liu, Jing; Lan, Ting; Yang, Guobin; Zhao, Zhengyu; Zhu, Peng; Sun, Hengqing; Cui, Xianze

doi:10.1016/j.asr.2015.03.042

Cited by 13 publications

(10 citation statements)

References 20 publications

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“…Figure 9 shows the regional maps of foF2 parameters extracted from the above inversion results through Kriging (KG) algorithm which has been widely used to reconstruct maps of ionospheric parameters [21,22,23]. When the spare date set is abundant, KG has good robustness [24]. (Although different models are used in vertical and oblique inversion, which may cause some inconsistencies between the electron density profiles, but as described in the article [19], the inversion results of foF2 parameters are quite reliable.…”

Section: Resultsmentioning

confidence: 99%

A Novel Ionospheric Sounding Network Based on Complete Complementary Code and Its Application

Liu

Yang

et al. 2019

Sensors

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In this paper, complete complementary code (CCC) sequences are applied to a High Frequency (HF) ionospheric sounding network. Ionosondes distributed at multiple locations use the mutually orthogonal CCC sequences to conduct vertical soundings synchronously. At the same time, thanks to the omnidirectional antennas, every station can receive the oblique echoes transmitted from the others. Due to the orthogonality between the code sequences, both vertical and oblique ionograms can be simultaneously obtained and completely separated. Through this method, the sounding efficiency can be enhanced, and the inversion difficulty can be reduced. Further, by using the data assimilation method, vertical and oblique sounding results can be combined to obtain a wide range of regional ionospheric characteristics. To verify the performance of this kind of sounding network, validation experiments are implemented to demonstrate that vertical and oblique ionograms can be obtained independently at the same time and inverted separately and that the maps of foF2 parameters obtained by using the data assimilation method provide more details than single vertical or oblique sounding.

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Section: Resultsmentioning

confidence: 99%

A Novel Ionospheric Sounding Network Based on Complete Complementary Code and Its Application

Liu

Yang

et al. 2019

Sensors

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“…It uses known sample values and variogram to determine unknown values at different spatial locations. The variogram function of Kriging method describes the spatial correlation among the measured samples used in the interpolation and are calculated by taking the difference between pairs of measurements for a given distance [4,24,53]. Splines and kriging are two methods that should be used alternately, depending on what one wants to obtain [59].…”

Section: Spatial Characteristics Reconstructionmentioning

confidence: 99%

“…Compared with global models, regional models give in general better results with better agreement with observations [8,24,25]. The development of these regional models arose: (1) With the demand for improved performances for specific areas, (2) in response to the availability of denser network of stations, and (3) to simplify the complex ionospheric morphology over a restricted area [25].…”

Section: Introductionmentioning

confidence: 99%

“…The development of these regional models arose: (1) With the demand for improved performances for specific areas, (2) in response to the availability of denser network of stations, and (3) to simplify the complex ionospheric morphology over a restricted area [25]. Thus, related work has also been continuing in more regional refined studies, such as the simplified ionospheric regional model (SIRM) [26] and its improved version [25] over Europe, the regional ionospheric model over Antarctica [27], the regional ionospheric model over India [28], the maps over northern China [24], as well as others [29]. The accuracy of the derived parameters in above-mentioned applications directly depends on the accuracy of f o F 2 [4].…”

Section: Introductionmentioning

confidence: 99%

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Simplified Regional Prediction Model of Long-Term Trend for Critical Frequency of Ionospheric F2 Region over East Asia

et al. 2019

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To improve the accuracy of predictions and simplify the difficulty with the algorithm, a simplified empirical model is proposed in developing a long-term predictive approach in determining the ionosphere’s F2-layer critical frequency (foF2). The main distinctive features introduced in this model are: (1) Its vertical incidence sounding data, which were obtained from 18 ionosonde stations in east Asia between 1949 and 2017, used in reconstructing the model and verification; (2) the use of second-order polynomial and triangle harmonic functions, instead of linear ones, to obtain the relationship between the seasonal vs. solar-cycle variations of foF2 and solar activity parameters; (3) the flux of solar radio waves at 10.7 cm and sunspot number are together introduced in reconstructing the temporal characteristics of foF2; and (4) the use of the geomagnetic dip coordinates rather than geographic coordinates in reconstructing the spatial characteristics of foF2. The statistical results reveal that foF2 values calculated from the proposed model agree well with the trend in the monthly median statistical characteristics obtained from measurements. The results are better than those obtained from the International Reference Ionosphere model using both the CCIR and URSI coefficients. Furthermore, the proposed model has enabled some useful guidelines to be established for a more complete and accurate Asia regional or global model in the future.

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“…Two important issues arise while applying modeling techniques to real applications: select the most powerful modeling approach (e.g., in terms of prediction) and better understand the problem under study. A large number of scientific papers are focused on comparing ANNs and Kriging models , but we have found only a few theoretical works showing their equivalence under certain conditions. For example, R. M. Neal (1996) demonstrated that some Bayesian regression models based on neural networks converge to Gaussian processes as the number of neurons in the hidden layer tends to infinity.…”

Section: Computer‐aided Experimental Approachmentioning

confidence: 99%

A computer‐aided methodology for the optimization of electrostatic separation processes in recycling

Borrotti

Pievatolo

Critelli

et al. 2015

Appl Stoch Models Bus & Ind

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The rapid growth of technological products has led to an increasing volume of waste electrical and electronic equipments (WEEE), which could represent a valuable source of critical raw materials. However, current mechanical separation processes for recycling are typically poorly operated, making it impossible to modify the process parameters as a function of the materials under treatment, thus resulting in untapped separation potentials. Corona electrostatic separation (CES) is one of the most popular processes for separating fine metal and nonmetal particles derived from WEEE. In order to optimize the process operating conditions (i.e., variables) for a given multi-material mixture under treatment, several technological and economical criteria should be jointly considered. This translates into a complex optimization problem that can be hardly solved by a purely experimental approach. As a result, practitioners tend to assign process parameters by few experiments based on a small material sample and to keep these parameters fixed during the process life-cycle. The use of computer experiments for parameter optimization is a mostly unexplored area in this field. In this work, a computer-aided approach is proposed to the problem of optimizing the operational parameters in CES processes. Three metamodels, developed starting from a multi-body simulation model of the process physics, are presented and compared by means of a numerical and simulation study. Our approach proves to be an effective framework to optimize the CES process performance. Furthermore, by comparing the predicted response surfaces of the metamodels, additional insight into the process behavior over the operating region is obtained.

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Comparison of the Kriging and neural network methods for modeling foF2 maps over North China region

Cited by 13 publications

References 20 publications

A Novel Ionospheric Sounding Network Based on Complete Complementary Code and Its Application

A Novel Ionospheric Sounding Network Based on Complete Complementary Code and Its Application

Simplified Regional Prediction Model of Long-Term Trend for Critical Frequency of Ionospheric F2 Region over East Asia

A computer‐aided methodology for the optimization of electrostatic separation processes in recycling

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