التنبؤ بمعدلات درجات الحرارة الشهرية العظمى في مدينة الموصل باستخدام الشبكة العصبية متعددة الطبقات
Year: 2020
Type: article
Abstract: Temperatures forecasting are very important warnings to protect life and property ,the research deals with the multi-layered neural network to predict the averages of maximum temperatures for Mosel synoptic station .This network proved its capability and qualification in prediction by comparison with other statistical procedures like Box-Jenkines model (SARIMA) .The research deals with two major problems , First , determining the number of input nodes .second , determining the hidden nodes in the layer .The results show the importance and qualification of the network through the proper choosing for number of input nodes and number of hidden nodes in the layer, by using the statistical techniques AIC , BIC , and ALCc .The maximum temperature averages for Mosel station has predicted for 2013 by using the previous statistical procedures and compared its results with the real measured data for the station , by using the MSE , MDE , and PMC statistical techniques .then we choose the best model to calculate the future predictions of temperatures averages for 2014 .
Author: عبد الجبار كاظم, احلام
Language: Arabic
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W7220499933