An Optimization Research of Evaporation Duct Prediction Models Based on a Deep Learning Method

Zhu, Xiaoyu; Zhu, Min; Li, Jincai; Jiang, Zhuhui; Li, Yinglun

doi:10.1109/imcec.2018.8469552

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…The superiority of the machine learning method in ICRAIC-2021 Journal of Physics: Conference Series 2203 (2022) 012079 IOP Publishing doi:10.1088/1742-6596/2203/1/012079 6 evaporation duct research was discovered. Zhu Xiaoyu further used MLP to train and test the model with the measured data in the South China Sea and built a pure data-driven SCS-MLP evaporation duct height prediction model [13]. At the same time, Zhu Xiaoyu used the output value of P-J model as the label value of MLP model, and optimized the P-J model in this way to get MLP P-J model.…”

Section: Application Of Machine Learning In Evaporation Duct Researchmentioning

confidence: 99%

Application of Machine Learning in the Research of Evaporation Duct Height: A review

Chai

Zhao

et al. 2022

J. Phys.: Conf. Ser.

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Evaporation duct is a kind of special atmospheric stratification that frequently appears on the sea surface, which has an important influence on the propagation and attenuation of electromagnetic waves, and is an important factor affecting the efficiency of marine radars and communication equipment. After the development in more than half a century, evaporation duct height can be obtained by direct detection, theoretical model, inversion and machine learning. Machine learning can explore the hidden laws of data efficiently and has the potential to surpass the traditional theoretical model. In this paper, the Machine Learning methods in evaporation duct research are shown and prospects of machine learning methods in evaporation duct research are given.

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Section: Application Of Machine Learning In Evaporation Duct Researchmentioning

confidence: 99%

Application of Machine Learning in the Research of Evaporation Duct Height: A review

Chai

Zhao

et al. 2022

J. Phys.: Conf. Ser.

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“…This model is a pure data driven evaporation duct prediction model, MLP model, which is built by Zhu Xiaoyu et al [38], [39] based on multi-layer perception mechanism. The following is a brief introduction.…”

Section: Evaporation Duct Prediction Model For Multilayer Perceptronmentioning

confidence: 99%

“…Based on the data of hydrometeorological and EDH measured during a voyage, a pure data-driven prediction model of XGBoost evaporation duct was proposed in this paper by using decision tree algorithm XGBoost (XGB model) for the first time. In consideration of performance comparison of MLP and XGBoost have been applied to a land cover urban classification [38], MLP model proposed by Zhu X. Y. et al [36], [37] and traditional PJ model are introduced in the contrast experiment. The predicted results based on XGB model, MLP model and PJ model were compared with the measured EDH of the voyage.…”

Section: Introductionmentioning

confidence: 99%

XGB Model : Research on Evaporation Duct Height Prediction Based on XGBoost Algorithm

Zhao¹,

Li²,

Zhao³

et al. 2020

RADIOENGINEERING

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Evaporation duct is a specific atmospheric structure at sea, which has an important influence on the propagation path of electromagnetic waves (EW). Considering the limit of existing evaporation duct height (EDH) prediction models and aiming at proposing more accurate and stronger generalization ability of EDH models, we applied eXtreme Gradient Boosting (XGBoosting) algorithm to the field of evaporation duct for the first time. And we proposed the new EDH prediction model using XGBoost algorithm (XGB model). Simultaneously, traditional Paulus-Jeske (PJ) model and deep learning Multilayer Perceptron (MLP) model were introduced into the experiment to make a comparison. In terms of comprehensive performance, XGB model is optimal in all sub-regions and total area. Finally, cross-learning experiments were carried out to test the generalization ability of XGB model. The results show that the generalization ability of XGB model is better than that of MLP model.

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“…The comparison results show that LS‐SVM is the most accurate of the three methods. Zhu et al () improved PJ model by using support vector machine regression (SVR), they obtained the SVR_PJ Model with higher prediction accuracy. Zhu et al () constructed a South China Sea (SCS) evaporation duct model perceptron (MLP).…”

Section: Introductionmentioning

confidence: 99%

“…We studied the prediction models using Gradient Boosting Regression algorithm (GBR) and proposed the PDD_GBR model and proposed GBR_PJ and GBR_BYC models by combining the results of traditional models with GBR. The above three models, PJ model, and the SVR_PJ model proposed by Zhu et al (), were compared and analyzed. The results show that the comprehensive performance of PDD_GBR model is the optimal among the five models, which possesses a high stability and has a great promotion compared with the exiting PJ and SVR_PJ models.…”

Section: Introductionmentioning

confidence: 99%

PDD_GBR: Research on Evaporation Duct Height Prediction Based on Gradient Boosting Regression Algorithm

Zhao

et al. 2019

Radio Science

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Evaporation duct is a special atmospheric stratification that always exists at sea, which has an important influence on electromagnetic wave propagation. Accurate prediction of evaporation duct height is of great significance for radio information system. In the era of big data, machine learning has been widely used in various research work and achieved a series of excellent results. Based on the observation data method, this paper studies the prediction model using Gradient Boosting Regression algorithm (GBR) and proposes the pure data‐driven GBR (PDD_GBR), GBR_Paulus‐Jeske (GBR_PJ), and GBR_Babin‐Young‐Carton (GBR_BYC) evaporation duct prediction models. Simultaneously, traditional Paulus‐Jeske (PJ) model, Babin‐Young‐Carton (BYC) model, and existing SVR_PJ model are introduced into the experiment to make a comparison. The comprehensive performance of PDD_GBR model is optimal among these models with high stability and strong generalization ability, whose prediction accuracy has a great promotion compared with other models.

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An Optimization Research of Evaporation Duct Prediction Models Based on a Deep Learning Method

Cited by 6 publications

References 12 publications

Application of Machine Learning in the Research of Evaporation Duct Height: A review

Application of Machine Learning in the Research of Evaporation Duct Height: A review

XGB Model : Research on Evaporation Duct Height Prediction Based on XGBoost Algorithm

PDD_GBR: Research on Evaporation Duct Height Prediction Based on Gradient Boosting Regression Algorithm

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