Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models Academic Article uri icon

abstract

  • 2019 Royal Meteorological Society The application of a novel method of adaptive neuro-fuzzy inference system (ANFIS) for the prediction of air temperature is investigated. The paper discusses the improvement of the ANFIS when used with genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR) and differential evolution (DE). For this purpose, three input of multiple variables are selected in order to predict monthly minimum, average and maximum air temperatures for 34 meteorological stations in Iran. The co-efficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and NashSutcliffe efficiency (NSE) are used as evaluation criteria. A comparison of suggested fuzzy models indicates that the ANFIS with the GA has the best performance in the prediction of maximum temperatures. It decreases the RMSE of the classic ANFIS model in the validation stage from 1.22 to 1.12C for Mashhad, from 1.26 to 1.01C for Zahedan, from 1.20 to 0.98C for Ahvaz, from 1.76 to 1.24C for Rasht and from 1.21 to 0.95C for Tabriz.

published proceedings

  • METEOROLOGICAL APPLICATIONS

author list (cited authors)

  • Azad, A., Kashi, H., Farzin, S., Singh, V. P., Kisi, O., Karami, H., & Sanikhani, H.

citation count

  • 23

complete list of authors

  • Azad, Armin||Kashi, Hamed||Farzin, Saeed||Singh, Vijay P||Kisi, Ozgur||Karami, Hojat||Sanikhani, Hadi

publication date

  • January 2020

publisher