  In this paper, preliminary results from the artificial neural network (ANN)
based model developed at IIT Indore has been presented. One year's hourly total
electron content (TEC) database has been created from the International
Reference Ionosphere (IRI) 2016 model. For the first time, a reverse problem
has been addressed, wherein the training has been performed for predicting the
three indices: 13-month running sunspot number, ionospheric index, and daily
solar radio flux also called targets to the network when hourly TEC values are
the inputs. The root mean square errors (RMSEs) of these targets have been
compared and minimized after several training of the dataset using different
sets of combinations. Unknown data fed to the network yielded 0.99%, 3.12%, and
0.90% errors for Rz12, IG12, and F10.7 radio flux, respectively, thus
signifying ~97% prediction accuracy of the model.
