Statistical and machine learning ensemble modelling to forecast sea surface temperature

Wolff, Stefan; O’Donncha, Fearghal; Chen, Bei

doi:10.1016/j.jmarsys.2020.103347

Cited by 60 publications

(39 citation statements)

References 27 publications

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“…As mentioned earlier, deep learning based models such as "classic" LSTM, CNN and their improved versions have attracted significant attention lately within the research community in this field, with studies comparing classic LSTM and CNN for predicting SST in different regions (Han et al 2019;Wolff et al 2020) and other researchers focusing on enhancing the performance of classic CNN and comparing it with other soft computing models (Barth et al 2020;Saha and Chauhan 2020;Yu et al 2020b;Zhang et al 2020b).…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Haghbin

Sharafati

Motta

et al. 2021

Prog Earth Planet Sci

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The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Haghbin

Sharafati

Motta

et al. 2021

Prog Earth Planet Sci

View full text Add to dashboard Cite

show abstract

“…Faced with a more difficult monitoring environment, applications of machine learning to aquaculture have developed slower. Many studies have investigated how machine learning could improve ocean monitoring and forecasting either by mining large ocean datasets Gokaraju et al (2011) or relating future conditions to historical observations (Wolff et al, 2020). More recently, the applications of machine learning and computer vision technology to aquaculture is receiving a lot of attention.…”

Section: Related Workmentioning

confidence: 99%

Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

et al. 2021

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Aquaculture, or the farmed production of fish and shellfish, has grown rapidly, from supplying just 7% of fish for human consumption in 1974 to more than half in 2016. This rapid expansion has led to the growth of Precision Aquaculture concept that aims to exploit data-driven management of fish production, thereby improving the farmer's ability to monitor, control, and document biological processes in farms. Fundamental to this paradigm is monitoring of environmental and animal processes within a cage, and processing those data toward farm insight using models and analytics. This paper presents an analysis of environmental and fish behaviour datasets collected at three salmon farms in Norway, Scotland, and Canada. Information on fish behaviour were collected using hydroacoustic sensors that sampled the vertical distribution of fish in a cage at high spatial and temporal resolution, while a network of environmental sensors characterised local site conditions. We present an analysis of the hydroacoustic datasets using AutoML (or automatic machine learning) tools that enables developers with limited data science expertise to train high-quality models specific to the data at hand. We demonstrate how AutoML pipelines can be readily applied to aquaculture datasets to interrogate the data and quantify the primary features that explains data variance. Results demonstrate that variables such as temperature, wind conditions, and hour-of-day were important drivers of fish motion at all sites. Further, there were distinct differences in factors that influenced in-cage variations driven by local variables such as water depth and ambient environmental conditions (particularly dissolved oxygen). The framework offers a transferable approach to interrogate fish behaviour within farm systems, and quantify differences between sites.

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“…The model is compound by three blocks: (i) a wavelet transformation plus the addition of Gaussian noise to enhance the robustness of the model for data transformation; (ii) an LSTM and a convolutional layer for feature extraction; (iii) and a fully connected layer for the prediction output. MLP and LSTM, together with a suite of ML models, including linear regression and decision tree are tested in [104] to estimate sea surface temperatures. The authors in [105] develop a DL framework for sea surface temperature forecasting associated with a tropical instability wave within the eastern equatorial Pacific Ocean.…”

Section: Sea Surface Temperaturementioning

confidence: 99%

Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting

et al. 2021

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An appropriate management of the available resources within oceans and coastal regions is vital to guarantee their sustainable development and preservation, where water quality is a key element. Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim. In this paper, we review methodologies and technologies for water quality assessment that contribute to a sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for water quality estimation and forecasting. The analyzed literature is classified depending on the type of task, scenario and architecture. Moreover, several applications including coastal management and aquaculture are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies are expected to be the main involved agents.

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Statistical and machine learning ensemble modelling to forecast sea surface temperature

Cited by 60 publications

References 27 publications

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting

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