Predicting temperature of Erbil City applying deep learning and neural network

<span>One of the most significant and daunting activities in today's world is temperature prediction. The meteorologists traditionally predict via some statistical models aimed to forecast fluctuations that might have happened atmospheric parameters such as temperature, humidity, etc. main objective this paper build an intelligent prediction model Erbil city KRG/ Iraq based on a historical dataset from 1992 2016 each year there are twelve months’ average readings (January December). Hence resolve problem up-to-date deep learning neural network has been used, <span id="docs-internal-guid-850bd062-7fff-c6c8-9146-ba6427eb24e0" style="font-size: 9pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">long short-term memory</span> (LSTM) artificial recurrent (RNN) architecture which employed estimate future temperature. implementing uses real-time 30 weather stations deployed area city. performance proposed compared with state art algorithms like Adeline network, Autoregressive (NAR), id="docs-internal-guid-14d37b98-7fff-0f76-848f-ad9f89224f77" pre-wrap;"> generalized regression network</span> (GRNN). results show gives minimum error.</span>