Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment
| Autor: | Davide Motta, Masoud Haghbin, Ahmad Sharafati, Mohamadreza Hosseinian Moghadam Noghani, Nadhir Al-Ansari |
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| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Relation (database) Computer science 0211 other engineering and technologies F700 02 engineering and technology Geotechnical Engineering computer.software_genre 01 natural sciences Fuzzy logic Sea Surface Temperature Biogeosciences 021101 geological & geomatics engineering 0105 earth and related environmental sciences Soft computing Hydrogeology Artificial neural network lcsh:QE1-996.5 lcsh:Geography. Anthropology. Recreation G900 Field (geography) lcsh:Geology Sea surface temperature Geoteknik lcsh:G General Earth and Planetary Sciences Data mining Prediction computer |
| Zdroj: | Progress in Earth and Planetary Science, Vol 8, Iss 1, Pp 1-19 (2021) |
| ISSN: | 2197-4284 |
| Popis: | 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. Validerad;2021;Nivå 2;2021-01-11 (alebob) |
| Databáze: | OpenAIRE |
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