The Global Positioning System (GPS) applications are highly vulnerable to the ionospheric space weather effects. Modelling and forecasting the ionospheric effects such as time delays for GPS signals are important for real time alerts of space weather effects on GPS services. In the present work, the performance of Neural Networks (NN) model is compared with International Reference Ionosphere (IRI) models. The ionospheric Total Electron Content (TEC) observations have been collected during 2015 year, descending phase of 24th solar cycle, over three Indian low latitude GPS stations namely, Bengaluru (Geographic coordinates: 13.02°N and 77.57°E), near to geomagnetic equator, Guntur (Geographic coordinates: 16.37°N and 80.37°E), which is at Equatorial Ionization Anomaly (EIA) and Lucknow (Geographic coordinates: 26.83°N and 80.92°E), which is beyond EIA region. The performance of NN model in predicting the ionospheric TEC values is compared with IRI (IRI-2012 and IRI-2016) models during test period, October–December 2015 over three Indian low latitude regions. It is observed that IRI models (IRI-2012 and IRI-2016) have shown more the temporal differences with GPS-VTEC during sunrise hours compared to sunset hours over three low latitude regions. The performance of IRI-2016 model has apparently better than IRI-2012 model. However, it is observed that IRI-2016 model has large discrepancies over Bengaluru and Guntur station due to high VTEC fluctuations at equatorial and low latitudes. The NN models are well predicted the measured diurnal mean VTEC variations with the less errors, ±5 TECU but the differences of IRI models are ±15 TECU over all the three stations. Later, GPS-data for 10 years, 2009–2018, is collected over Bengaluru station during 24th solar cycle. The performance of NN model is validated during 2016, 2017 and 2018 years over Bengaluru GPS station. The error measurements and experimental results reported that the measured GPS-VTEC values are well predicted by NN model compared to IRI-2016 model over equatorial and low latitude GPS stations.

全球定位系统（GPS）应用非常容易受到电离层空间天气影响。对电离层效应（例如GPS信号的时延）进行建模和预测对于实时预警空间天气对GPS服务的影响非常重要。在当前的工作中，将神经网络（NN）模型的性能与国际参考电离层（IRI）模型进行了比较。电离层总电子含量（TEC）观测值是在2015年（第24个太阳周期的下降阶段）收集的，这三个印度低纬度GPS站位于班加罗尔（地理坐标：北纬13.02°E和东经77.57°E），地磁赤道附近，位于赤道电离异常（EIA）和勒克瑙（地理坐标：26.83°N和80.92°E）的Guntur（地理坐标：16.37°N和80.37°E），超出EIA区域。在三个印度低纬度地区的测试期间（2015年10月至12月），将NN模型在预测电离层TEC值方面的性能与IRI（IRI-2012和IRI-2016）模型进行了比较。可以看出，IRI模型（IRI-2012和IRI-2016）在三个低纬度地区的日出时间与日落时间相比，在GPS-VTEC上显示出更多的时空差异。IRI-2016模型的性能明显优于IRI-2012模型。然而，据观察，由于赤道和低纬度地区的VTEC波动较大，因此IRI-2016模型在班加罗尔和冈图尔站存在较大差异。NN模型可以很好地预测测得的日平均VTEC变化，误差较小，为±5 TECU，但在所有三个站点上，IRI模型的差异为±15 TECU。后来，2009年至2018年10年的GPS数据是在第24个太阳周期的班加罗尔站收集的。NN模型的性能已在2016年，2017年和2018年期间在班加罗尔GPS站进行了验证。误差测量和实验结果表明，与IRI-2016模型相比，在赤道和低纬度GPS站上，NN模型可以很好地预测GPS-VTEC的测量值。