Title: A modelling study for predicting temperature and precipitation variations

Authors: Azadeh Navazi; Abdolreza Karbassi; Shapour Mohammadi; Seyed Masoud Monavari; Saeed Motesaddi Zarandi

Addresses: Department of Environmental Science, Science and Research Branch, Islamic Azad University, Tehran, Iran ' Faculty of Environment, University of Tehran, P.O. Box 14155-6135, Tehran, Iran ' Faculty of Management, Tehran University School of Management, University of Tehran, North Kargar, Adjacent Paul Nasr, Tehran, Iran ' Department of Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran ' Department of Environmental Health Engineering, School of Health, Shahid Beheshti University of Medical Sciences, Velenjak St., Tehran, Iran

Abstract: The trends of climate change and consequently global warming have put pressure on the urban environment and have led to serious environmental damages. The main objective of this study is to make a ten-year prediction of climatic parameters in Tehran in order to identify the impacts of climate change on urban environments and provide adaptation strategies to be used in future studies. For this purpose, artificial neural network (ANN) algorithms were employed. For the first time, a 30-year mean data of 'wind speed', 'dry-bulb temperature', 'wet-bulb temperature', 'mean daily temperature', 'dew point temperature', 'relative humidity' and 'precipitation' were investigated for their ten-year prediction using feed-forward ANN and back propagation algorithm. The root mean square error (RMSE) of the mentioned parameters in optimisation model was obtained to be 0.0307, 0.0625, 0.0566, 0.0382, 0.0214, 0.0758 and 0.0466, respectively. Annual temperature rise of 0.04°C and annual precipitation increase of 3.4 mm were also found to be likely to occur in Tehran by 2021.

Keywords: climate change; global warming; prediction; urban environment; climatic parameters; artificial neural network; ANN.

DOI: 10.1504/IJGW.2017.083666

International Journal of Global Warming, 2017 Vol.11 No.4, pp.373 - 389

Received: 11 Oct 2014
Accepted: 28 Mar 2015

Published online: 19 Apr 2017 *

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