توسعه یک مدل برای پیش بینی جزیره حرارتی شهری با استفاده از شبکه عصبی

This study presents the applicability of artificial neural networks (ANNs) and learning paradigms for UHI intensity prediction in Athens, Greece. The proposed model is tested using Elman, Feed-Forward and Cascade neural network architecture. The data of time, ambient temperature and global solar radiation are used to train and test the different models. The prediction accuracy is analyzed and evaluated.