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

Masoud Haghbin; Ahmad Sharafati; Davide Motta; Nadhir Al-Ansari; Mohamadreza Hosseinian Moghadam Noghani

doi:10.1186/s40645-020-00400-9

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

Masoud Haghbin, Ahmad Sharafati^*, Davide Motta, Nadhir Al-Ansari, Mohamadreza Hosseinian Moghadam Noghani

^*Corresponding author for this work

Mechanical and Construction Engineering Department

Research output: Contribution to journal › Review article › peer-review

40 Citations (Scopus)

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Abstract

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.

Original language	English
Article number	4
Number of pages	19
Journal	Progress in Earth and Planetary Science
Volume	8
Issue number	1
Early online date	5 Jan 2021
DOIs	https://doi.org/10.1186/s40645-020-00400-9
Publication status	Published - Dec 2021

Keywords

Prediction
Sea Surface Temperature
Soft computing

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s40645-020-00400-9Final published version, 1.34 MBLicence: CC BY

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

abstract = "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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AU - Haghbin, Masoud

AU - Sharafati, Ahmad

AU - Motta, Davide

AU - Al-Ansari, Nadhir

AU - Noghani, Mohamadreza Hosseinian Moghadam

PY - 2021/12

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N2 - 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.

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

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