Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

  • Armin Azad Semnan University
  • Jamshid Pirayesh Semnan University
  • Saeed Farzin Semnan University
  • Leila Malekani University of Tabriz
  • Sheida Moradinasab Semnan University
  • Ozgur Kisi Ilia State University
Keywords: ANFIS, ANN, genetic algorithm, particle swarm algorithm, long-term air temperatures

Abstract

Traditionally, climate conditions has been one of the influential factors in population growth in worldwide. Hence, predicting these conditions can be an important step to improve life conditions in worldwide. In this study, application of genetic algorithm (GA) and particle swarm algorithm (PSO) were considered as alternatives to available algorithms for training artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict air temperature. Therefore, monthly minimum, average and maximum air temperatures of Tehran-Iran station at 64-years (1951-2014) were selected as predicted time-series. Firstly, the most appropriate inputs were selected for models using sensitivity analysis. After that, long-term air temperatures (1 month, 1, 2 and 3 years ahead) were modeled.  Results showed that: 1) the given algorithms had acceptable results in improving the models’ performance in modeling minimum, mean and maximum air temperatures. Also, they could improve the performance of ANN and ANFIS in most of the prediction intervals, 2) ANFIS-GA was selected as the most suitable model so that its average determination coefficient (R2), root mean square errors (RMSE) and mean absolute errors (MAE) were 0.88, 1.41 and 2.52, respectively, 3) the sensitivity analysis provided suitable results in selecting the most appropriate model inputs for forecasting the minimum, mean and maximum air temperatures in different intervals.

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Author Biographies

Armin Azad

Faculty of Civil Engineering, Semnan University, Semnan, Iran

Jamshid Pirayesh

Faculty of Electronic Engineering, Semnan University, Semnan, Iran

Saeed Farzin

Faculty of Civil Engineering, Semnan University, Semnan, Iran

Leila Malekani

Faculty of Civil Engineering, University of Tabriz, Iran

Sheida Moradinasab

Faculty of Chemistry, Semnan University, Semnan, Iran

Ozgur Kisi

School of Technology, Ilia State University, Tbilisi, Georgia

Published In
Vol 23 No 6, Nov 30, 2019
How to Cite
[1]
A. Azad, J. Pirayesh, S. Farzin, L. Malekani, S. Moradinasab, and O. Kisi, “Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures”, Eng. J., vol. 23, no. 6, pp. 83-98, Nov. 2019.