A High-Accuracy and Fast Retrieval Method of Atmospheric Parameters Based on Genetic-BP

Tian, Jia-sheng; Shi, Junpeng

doi:10.1109/access.2022.3151868

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…The radar altimeter measures the SSH by the echo time between the satellite and the ocean surface. However, due to the comprehensiveness and complexity of the ocean dynamic environment, the spatial and temporal distribution of water vapor and cloud liquid water content is uneven, which introduces the wet troposphere path delay for the radar altimeter [1,2]. Generally, the wet troposphere path delay will lead to a 3~50 cm uncertainty error for SSH measurements with radar altimetry, which is one of the major error sources in radar altimetry applications.…”

Section: Introductionmentioning

confidence: 99%

On-Orbit Calibration and Wet Tropospheric Correction of HY-2C Correction Microwave Radiometer

et al. 2023

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HY-2C is the third satellite in China’s ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (TB) of CMR to retrieve path delay, an on-orbit calibration effort is required. In this study, an antenna pattern correction (APC) method and a neural network method are used to perform an on-orbit calibration for CMR’s antenna temperatures and a model based on the Whale Optimization Algorithm (WOA), Levenberg–Marquardt (LM) algorithm, and Back-Propagation neural network (WOA–LM–BP) has been proposed to retrieve the wet tropospheric correction (WTC) of CMR. The on-orbit calibration results, compared with the simulated brightness temperatures calculated by the radiative transfer model (RTM), have shown that compared with the APC method, the neural network method can almost eliminate the latitude variation, and the total bias and standard deviation of the on-orbit calibrated TB at all channels have obviously decreased. The retrieved WTC results also have shown that the retrieved WTC of CMR has a good agreement with the corresponding ones from the model-derived WTC and Jason-3.

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Section: Introductionmentioning

confidence: 99%

On-Orbit Calibration and Wet Tropospheric Correction of HY-2C Correction Microwave Radiometer

et al. 2023

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“…This was first proposed by Rumelhart and Mccelland in 1986 [13], and it is beginning to be used in various fields because of its good self‐learning ability and self‐adaptive capability, such as the information field, financial field, and network security field. Tian applied BP neural networks to the inversion of atmospheric parameters [14]. Zhong used the classifier function of a BP neural network to perform a fault diagnosis of equipment faults [15].…”

Section: Introductionmentioning

confidence: 99%

An improved evaluation model for supplier selection based on particle swarm optimisation‐back propagation neural network

Wang

et al. 2022

IET Collab Intel Manufact

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With the trend of supply chain globalisation, competition among enterprises is becoming more intense. Enterprises urgently need to improve their core competitiveness, and the enhancement of the competencies can depend on technologies services and the quality of suppliers. Since external factors are less controllable, this study starts with the quality of suppliers and proposes a supplier evaluation method that combines particle swarm optimisation with neural network algorithm to maximise the interests of enterprises. The particle swarm algorithm to lock the approximate location of the global optimum is first employed. Based on this, we establish an evaluation model of suppliers to train for the minimum errors between the desired and predicted values by constructing a back propagation (BP) neural network. Finally, the output results of the proposed method is compared with the BP neural network without the particle swarms optimisation. The proposed model is less empirically sensitive to the initialisation and can quickly converge to the local optimums, which overcomes the shortage of traditional neural networks and is more applicable to supplier evaluation.

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Research on Correction of Temperature and Mineralization and Prediction of Water Holdup Based on Machine Learning

Dai,

Wei,

Gan

et al. 2024

IEEE Access

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A High-Accuracy and Fast Retrieval Method of Atmospheric Parameters Based on Genetic-BP

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 3 publications

References 46 publications

On-Orbit Calibration and Wet Tropospheric Correction of HY-2C Correction Microwave Radiometer

On-Orbit Calibration and Wet Tropospheric Correction of HY-2C Correction Microwave Radiometer

An improved evaluation model for supplier selection based on particle swarm optimisation‐back propagation neural network

Research on Correction of Temperature and Mineralization and Prediction of Water Holdup Based on Machine Learning

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