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

Research output: Contribution to journalReview articlepeer-review

5 Citations (Scopus)

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 languageEnglish
Article number4
Number of pages19
JournalProgress in Earth and Planetary Science
Volume8
Issue number1
Early online date5 Jan 2021
DOIs
Publication statusE-pub ahead of print - 5 Jan 2021

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