Earth's ionosphere is an important medium for navigation, communication, and radio wave transmission. The inadequate advances in technology do not allow enough realization of ionosphere monitoring systems globally, and most research is still limited to local research in certain parts of the world. However, new methods developed in the field of forecasting and calculation contribute to the solution of such problems. One of the methods developed is artificial neural networks-based deep learning method (DLM), which has become widespread in many areas recently and aimed to forecast ionospheric GPS-TEC variations with DLM. In this study, hourly resolution GPS-TEC values were obtained from five permanent GNSS stations in Turkey. DLM model is created by using the TEC variations and 9 different SWC index values between the years 2016 and 2018. The forecasting process (daily, three-daily, weekly, monthly, quarterly, and semi-annual) was carried out for the prediction of the TEC variations that occurred in the first half-year of 2019. The findings show that the proposed deep learning-based long short-term memory architecture reveals changes in ionospheric TEC estimation under 1–5 TECU. The calculated correlation coefficient and R² values between the forecasted GPS-TEC values and the test values are higher than 0.94.

地球电离层是导航，通讯和无线电波传输的重要介质。技术进步不足，无法在全球范围内充分实现电离层监测系统，而且大多数研究仍仅限于世界某些地区的本地研究。但是，在预测和计算领域开发的新方法有助于解决此类问题。开发的方法之一是基于人工神经网络的深度学习方法（DLM），该方法最近已在许多领域广泛使用，旨在利用DLM预测电离层GPS-TEC的变化。在这项研究中，每小时分辨率GPS-TEC值是从土耳其的五个永久性GNSS站获得的。DLM模型是使用2016年至2018年之间的TEC变化和9种不同的SWC指数值创建的。进行了预测过程（每天，每天，每周，每月，每季度，每季度和每半年一次）以预测在2019年上半年发生的TEC变化。研究结果表明，建议的深度学习基于长期的短期记忆架构，揭示了在1–5 TECU下电离层TEC估算的变化。计算出的相关系数和GPS-TEC预测值和测试值之间的R ²值大于0.94。