Research on evaporation duct height prediction based on back propagation neural network

Zhao, Wenpeng; Li, Jincai; Zhao, Jun; Jiang, Tao; Zhu, Junxing; Zhao, Dandan; Zhao, Juan

doi:10.1049/iet-map.2019.1136

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…They then showed that their approach significantly improved the P-J model. In 2019, Zhao Wenpeng et al also applied BP neural network to the prediction of evaporation duct height and achieved good results [25]. They further proposed a pure data-driven gradient lifting tree (GBDT) evaporation duct height prediction model, PDD_GBR, which significantly improved both the accuracy and regional generalization ability of their original model [26].…”

Section: High Accuracy Under Ideal Conditionsmentioning

confidence: 99%

LGB-PHY: An Evaporation Duct Height Prediction Model Based on Physically Constrained LightGBM Algorithm

Chai

Zhao

et al. 2022

Remote Sensing

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The evaporation duct is a special atmospheric stratification that significantly influences the propagation path of electromagnetic waves at sea, and hence, it is crucial for the stability of the radio communication systems. Affected by physical parameters that are not universal, traditional evaporation duct theoretical models often have limited accuracy and poor generalization ability, e.g., the remote sensing method is limited by the inversion algorithm. The accuracy, generalization ability and scientific interpretability of the existing pure data-driven evaporation duct height prediction models still need to be improved. To address these issues, in this paper, we use the voyage observation data and propose the physically constrained LightGBM evaporation duct height prediction model (LGB-PHY). The proposed model integrates the Babin–Young–Carton (BYC) physical model into a custom loss function. Compared with the eXtreme Gradient Boosting (XGB) model, the LGB-PHY based on a 5-day voyage data set of the South China Sea provides significant improvement where the RMSE index is reduced by 68%, while the SCC index is improved by 6.5%. We further carried out a cross-comparison experiment of regional generalization and show that in the sea area with high latitude and strong adaptability of the BYC model, the LGB-PHY model has a stronger regional generalization performance than that of the XGB model.

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Section: High Accuracy Under Ideal Conditionsmentioning

confidence: 99%

LGB-PHY: An Evaporation Duct Height Prediction Model Based on Physically Constrained LightGBM Algorithm

Chai

Zhao

et al. 2022

Remote Sensing

Self Cite

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“…Based on the above work, Zhao et al has tried a lot of machine learning models since 2019. He applied BP neural network to predict evaporation duct height [16], and experiments proved that compared with the traditional P-J model, BP neural network has a better effect than simple Support Vector Regression (SVR). Both precision and regional generalization have better performance.…”

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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“…e structure of the BP neural network is divided into input layer, hidden layer, and output layer. e input layer and output layer are both one-layer structure, but the hidden layer can be one or more [29][30][31][32][33][34][35]. In this article, we constructed a model with 3 hidden layers.…”

Section: Model Algorithm and Principlementioning

confidence: 99%

Research on the Route Pricing Optimization Model of the Car‐Free Carrier Platform Based on the BP Neural Network Algorithm

2021

Complexity

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The car-free carrier platform is a product of the rapid development of the modern logistics industry and has a vital strategic value for promoting the construction of a country’s comprehensive transportation. However, due to the unreasonable platform pricing model, the industry is currently in a bottleneck period. In order to solve this problem, we established a gray correlation model to calculate the degree of correlation between each characteristic index and platform pricing based on the massive historical transaction data of a certain platform and performed K-means clustering on the results to discover the main factors affecting platform pricing. Based on the abovementioned results, we created a pricing optimization model based on the BP neural network, with the structure of 8-13-1 to predict the freight pricing of the order and test the prediction results. The test shows that the goodness of fit (R2) of the predicted value is close to 1, and the prediction error range is less than 3.7%, which proves the accuracy and effectiveness of the BP neural network model and provides an effective reference for the optimization of the pricing model of the car-free carrier platform.

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Research on evaporation duct height prediction based on back propagation neural network

Cited by 13 publications

References 18 publications

LGB-PHY: An Evaporation Duct Height Prediction Model Based on Physically Constrained LightGBM Algorithm

LGB-PHY: An Evaporation Duct Height Prediction Model Based on Physically Constrained LightGBM Algorithm

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

Research on the Route Pricing Optimization Model of the Car‐Free Carrier Platform Based on the BP Neural Network Algorithm

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