Predicting temperature of Erbil City applying deep learning and neural network - Sardar M. R. K. Al- Jumur; Shahab Wahhab Kareem; Raghad Z. Yousif (Indonesian Journal of Electrical Engineering and Computer Science, 2021)
One of the most significant and daunting activities in today's world is temperature prediction. The meteorologists traditionally predict temperature via some statistical models aimed to forecast the fluctuations that might have happened to atmospheric parameters such as temperature, humidity, etc. The main objective of this paper is to build an intelligent temperature prediction model of Erbil city in KRG/ Iraq based on a historical dataset from 1992 to 2016 in each year there are twelve months’ average temperature readings from (January to December). Hence to resolve this prediction problem an up-to-date deep learning neural network has been used, the network model is based on long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture which employed to estimate the future average temperature. The implementing model uses the dataset from real-time 30 weather stations deployed in the area of the city. The prediction performance of the proposed recurrent neural network model has been compared with some state of art algorithms like Adeline neural network, Autoregressive neural network (NAR), and  generalized regression neural network (GRNN). The results show that the proposed model based on deep learning gives minimum prediction error.