A Preliminary Neural Network Model for Range Spread-F Events at Chumphon Station, Thailand

Abstract

In this work, we develop a preliminary neural network model for range-Type spread-F events over Chumphon station (10.7N latitude, 99.4E longitude), Thailand. The spread-F neural network model is designed with the input parameters including seasonal variations, diurnal variations, window-Averaged magnetic activity (Ap index) and window-Averaged solar activity (F10.7 index). The model is based on the ionogram data during the 24 th solar cycle from 2013 to 2016. As a result, the proposed model can provide the predicted results and the network performance of 97.8% for correct classification.