Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks

Durán-Rosal, Antonio Manuel; Hervás-Martínez, C.; Tallón‐Ballesteros, Antonio J.; Martínez-Estudillo, Alfonso Carlos; Salcedo‐Sanz, Sancho

doi:10.1016/j.oceaneng.2016.03.053

Cited by 27 publications

(11 citation statements)

References 45 publications

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“…In order to improve SPAMDA, some future work could be focused on new functional modules for managing meteorological data of different formats [69], so that the developed tool can be extended to any other research, new pre-processing functionalities such as filters to analyse the correlation between attributes or new functional modules for recovering missing values using nearby buoys data [70]. Furthermore, the developed software could manage other sources of reanalysis data (with different spatial and temporal resolution), and new output formats for the datasets which could be used as input by other tools for ML such as KEEL (Knowledge Extraction based on Evolutionary Learning) [71].…”

Section: Discussionmentioning

confidence: 99%

Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

et al. 2021

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Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented.

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Section: Discussionmentioning

confidence: 99%

Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

et al. 2021

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“…After obtaining interpolation results for the time and space dimensions, the BP (back propagation) neural network was trained to integrate spatial and temporal interpolation results to obtain final missing data estimation values [5]. The BP neural network can be regarded as a nonlinear function.…”

Section: Spatio-temporal Integrationmentioning

confidence: 99%

“…In past decades, a large number of interpolation methods has been proposed to solve the problem of spatio-temporal missing data [4][5][6][7][8][9][10]. These methods can be roughly divided into three categories: spatial interpolation, temporal interpolation and spatio-temporal interpolation.…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

Cheng

2017

IJGI

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Abstract:Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data; however, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. As a result, traditional methods often fail to obtain satisfactory accuracy or address high levels of complexity. To combat these problems, this study developed an effective two-step method for spatio-temporal missing data reconstruction (ST-2SMR). This approach includes a coarse-grained interpolation method for considering missing patterns, which can successfully eliminate the influence of continuous missing data on the overall results. Based on the results of coarse-grained interpolation, a dynamic sliding window selection algorithm was implemented to determine the most relevant sample data for fine-grained interpolation, considering both spatial and temporal heterogeneity. Finally, spatio-temporal interpolation results were integrated by using a neural network model. We validated our approach using Beijing air quality data and found that the proposed method outperforms existing solutions in term of estimation accuracy and reconstruction rate.

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“…These shortcomings have been addressed by developing machine learning (ML) models, which have proven to be robust, fast, and highly accurate [ 1 ]. For instance, Durán-Rosal et al [ 2 ] proposed using the evolutionary unit neural network (EPUNN) and the linear model as the input portion to reconstruct the data to meet the constantly changing data flow.…”

Section: Introductionmentioning

confidence: 99%

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Ali

Fathalla

Salah

et al. 2021

Computational Intelligence and Neuroscience

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Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model.

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Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks

Cited by 27 publications

References 45 publications

Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

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