Soft computing methods for geoidal height transformation

Akyılmaz, O.; Ozludemir, M. T.; Ayan, T.; Çeli̇k, Rahmi Nurhan

doi:10.1186/bf03353193

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…The lake level has risen rapidly over the last two decades (Lei et al, 2013), and it is still rising (Jiang et al, 2017a). This lake is chosen for two reasons; it is large enough, which allows us to solve short-wavelength variation and it is close to high mountains where the geoid models are affected by topographic effect (Akyilmaz et al, 2009;Forsberg, 1985).…”

Section: Study Sitementioning

confidence: 99%

Influence of local geoid variation on water surface elevation estimates derived from multi-mission altimetry for Lake Namco

Jiang

Andersen

Nielsen

et al. 2019

Remote Sensing of Environment

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Water surface elevation (WSE) is an essential quantity for water resource monitoring and hydrodynamic modeling. Satellite altimetry has provided data for inland waters. The height that is derived from altimetry measurement is ellipsoidal height. In order to convert the ellipsoidal height to orthometric height, which has physical meaning, accurate estimates of the geoid are needed. This paper evaluates the suitability of geodetic altimetric measurements for improvement of global geoid models over a large lake in the Tibetan Plateau. CryoSat-2 and SARAL/AltiKa are used to derive the high-frequency geoid correction. A validation of the local geoid correction is performed with data from in-situ observations, a laser altimetry satellite (ICESat), a Ka-band radar altimetry satellite (SARAL) and a SAR radar altimetry satellite (Sentinel-3). Results indicate that, geodetic altimetric dataset can capture the high-resolution geoid information. By applying local geoid correction, the precision of ICESat, SARAL and Sentinel-3 retrievals are significantly improved. We conclude that using geodetic altimetry to correct for local geoid residual over large lakes significantly decreases the uncertainty of estimates of WSE. These results also indicate the potential of geodetic altimetry missions to determine local geoid residual with centimeterlevel accuracy, which can be incorporated to improve global and regional geopotential models.

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Section: Study Sitementioning

confidence: 99%

Influence of local geoid variation on water surface elevation estimates derived from multi-mission altimetry for Lake Namco

Jiang

Andersen

Nielsen

et al. 2019

Remote Sensing of Environment

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“…These techniques are characterized by their ability to handle imprecise, uncertain, and incomplete information, making them well-suited for addressing the challenges associated with geodetic data processing. Soft computing techniques have demonstrated their potential in various geodetic data processing tasks such as satellite image classification [6], terrain modeling [7], and geoid determination [8,9]. They are particularly effective in pattern recognition, learning from data, and optimizing complex problems.…”

Section: Introductionmentioning

confidence: 99%

Geodetic Data Processing Using Soft Computing Techniques

Habib

2023

Applications of Remote Sensing

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Geodetic data processing involves using mathematical and computational techniques to analyze and interpret geospatial data about the Earth’s surface and the objects and features that exist on it. This data is collected through satellite imagery, aerial photography, and surveying instruments such as total stations and GPS receivers. The applications of geodetic data processing are diverse and include land surveying, mapping, navigation, environmental monitoring, and disaster management. It is crucial to understand and manage the Earth’s resources and address global challenges such as climate change, natural disasters, and urbanization. In recent years, the information technology industry has undergone a considerable transformation that has significantly impacted the development of various disciplines. Intelligent systems, powerful tools for understanding and solving complex engineering issues, have become increasingly important in this context. Soft computing techniques, including artificial neural networks, fuzzy logic, and evolutionary algorithms, are used more frequently in geodetic data processing due to their ability to handle complex, imprecise, and uncertain data. This study discusses using soft computing techniques in geodetic data processing and examines the challenges and future directions in using soft computing techniques in geodetic data processing.

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“…With these features, ANN is seen as a suitable method for geoid undulation determination. Studies have yielded successful results by using ANN (Stopar et al, 2006;Lin, 2007;Akyilmaz et al, 2009;Pikridas et al, 2011;Veronez et al, 2011;Akcin & Celik, 2013;Erol & Erol, 2013;Elshambaky, 2018;Kaloop et al, 2018;Albayrak et. al, 2020;Erol & Erol, 2021).…”

Section: Introductionmentioning

confidence: 99%

Geoid undulation prediction using ANNs (RBFNN and GRNN), multiple linear regression (MLR), and interpolation methods: A comparative study

Konakoǧlu

Akar

2022

Earth sci. res. j.

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The present work aimed to develop a prediction model to estimate geoid undulation and to compare its efficiency with other methods including radial basis function neural network (RBFNN), generalized regression neural network (GRNN), multiple linear regression (MLR) and, ten different interpolation methods. In this study, the k-fold cross-validation method was used to evaluate the model and its behavior on the independent dataset. With this validation method, each of a k number of groups has the chance to be divided into training and testing data. The performances of the methods were evaluated in terms of the root mean square error (RMSE) mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R2) and using graphical indicators. The evaluation of the performance of the datasets obtained using cross-validation was done in two ways. If we accept the method having the minimum error result as the most appropriate method, the natural neighbor (NN) method in the DS#5 dataset gave better results than the other methods (RMSE=0.14173 m, MAE=0.09729 m, NSE=0.98986, and R2=0.99011. On the other hand, it has been observed that, the GRNN method exhibited the best performance, on average, with RMSE=0.18539 m, MAE=0.13676 m, NSE=0.98229, and R2=0.98249.

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Soft computing methods for geoidal height transformation

Cited by 14 publications

References 12 publications

Influence of local geoid variation on water surface elevation estimates derived from multi-mission altimetry for Lake Namco

Influence of local geoid variation on water surface elevation estimates derived from multi-mission altimetry for Lake Namco

Geodetic Data Processing Using Soft Computing Techniques

Geoid undulation prediction using ANNs (RBFNN and GRNN), multiple linear regression (MLR), and interpolation methods: A comparative study

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