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Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction

Bhagat, Suraj Kumar; Tiyasha, Tiyasha; Al-khafaji, Zainab; Laux, Patrick ORCID iD icon 1; Ewees, Ahmed A.; Rashid, Tarik A.; Salih, Sinan; Yonaba, Roland ; Beyaztas, Ufuk; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher
1 Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT)

Abstract:

Air temperature (AT) prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system (DENFIS), hybrid neural-fuzzy inference system (HyFIS), and adaptive neurofuzzy inference system (ANFIS) for AT prediction. Modelling was done for three stations in North Dakota (ND), USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly variable data, such as AT, which shows a high positive correlation with average daily dew point (DP), total solar radiation (TSR), and negative correlation with average wind speed (WS). At the Robinson station, DENFIS performed the best with a coefficient of determination (R$^{2}$) of 0.96 and a modified index of agreement (md) of 0.92, followed by ANFIS with R$^{2}$ of 0.94 and md of 0.89, and HyFIS with R$^{2}$ of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R$^{2}$: 0.953/0.960, md: 0.903/0.912, then ANFIS with R$^{2}$: 0.943/0.942, md: 0.888/0.890, and HyFIS with R$^{2}$ 0.908/0.905, md: 0.845/0.821, respectively. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000151817
Veröffentlicht am 04.11.2022
Originalveröffentlichung
DOI: 10.1155/2022/1047309
Scopus
Zitationen: 8
Dimensions
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 23.09.2022
Sprache Englisch
Identifikator ISSN: 1099-0526, 1076-2787
KITopen-ID: 1000151817
HGF-Programm 12.11.33 (POF IV, LK 01) Regional Climate and Hydrological Cycle
Erschienen in Complexity
Verlag Hindawi
Band 2022
Seiten Art.Nr. 1047309
Nachgewiesen in Scopus
Dimensions
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