{"title":"梯度提升树和去噪自动编码器修正数值波浪预报","authors":"Ivan Yanchin, C. Guedes Soares","doi":"10.3390/jmse12091573","DOIUrl":null,"url":null,"abstract":"This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine learning model is based on gradient boosted trees, which is trained to predict the residue between the model’s forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. This paper demonstrates that this model can significantly reduce errors for all used geographical locations. This paper also uses SHapley Additive exPlanation values to investigate the influence that the numerically predicted wave parameters have when the machine learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, this paper proposes to use the denoising autoencoder to remove this noise from the numerical model’s prediction. The results demonstrate that denoising autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. This paper provides some explanations as to why this may happen.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gradient Boosted Trees and Denoising Autoencoder to Correct Numerical Wave Forecasts\",\"authors\":\"Ivan Yanchin, C. Guedes Soares\",\"doi\":\"10.3390/jmse12091573\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine learning model is based on gradient boosted trees, which is trained to predict the residue between the model’s forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. This paper demonstrates that this model can significantly reduce errors for all used geographical locations. This paper also uses SHapley Additive exPlanation values to investigate the influence that the numerically predicted wave parameters have when the machine learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, this paper proposes to use the denoising autoencoder to remove this noise from the numerical model’s prediction. The results demonstrate that denoising autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. This paper provides some explanations as to why this may happen.\",\"PeriodicalId\":16168,\"journal\":{\"name\":\"Journal of Marine Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Marine Science and Engineering\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3390/jmse12091573\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Marine Science and Engineering","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/jmse12091573","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
Gradient Boosted Trees and Denoising Autoencoder to Correct Numerical Wave Forecasts
This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine learning model is based on gradient boosted trees, which is trained to predict the residue between the model’s forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. This paper demonstrates that this model can significantly reduce errors for all used geographical locations. This paper also uses SHapley Additive exPlanation values to investigate the influence that the numerically predicted wave parameters have when the machine learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, this paper proposes to use the denoising autoencoder to remove this noise from the numerical model’s prediction. The results demonstrate that denoising autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. This paper provides some explanations as to why this may happen.
期刊介绍:
Journal of Marine Science and Engineering (JMSE; ISSN 2077-1312) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to marine science and engineering. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.