## 1 Introduction  
Pc5 geomagnetic pulsations are ultra-low frequency (ULF) waves in the frequency range of 1.5-7 mHz observed both in the ionosphere and on the ground [1]. They are the most frequently observed ULF waves because of their large amplitudes and the long periods within which they occur. The large amplitude and long periods of Pc5 waves significantly enhance energy transfer, particle modulation, magnetic field perturbations, and ionospheric current variations [2], all of which contribute to increased plasma irregularities and the likelihood of ionospheric scintillations in the sensitive equatorial anomaly region. Pc5 pulsations can be observed in auroral regions [3] and can penetrate to middle, low, and even equatorial latitudes [4; 5]. Penetration of Pc5 waves to lower latitudes is favored by several mechanisms such as the propagation of preliminary reverse impulse [6]. Recently, [PERSON]. [7] reported that Pc5 waves in the ionosphere can modulate a high-latitude electric field, which can penetrate to equatorial latitudes and cause density irregularities such as ionospheric scintillations in the ionosphere. Ionospheric scintillation refers to the abrupt changes in radio signal amplitude and phase [8]. Scintillations have diverse effects on communication and position systems [9], thus developing models for effective prediction of ionospheric scintillation will help mitigate the effects of the phenomenon. Several researchers have contributed to this quest by developing forecasting models utilizing machine learning techniques. Machine learning techniques are particularly well-suited for the prediction of ionospheric scintillations due to their ability to efficiently process large, complex datasets and identify non-linear patterns and correlations between multiple geophysical variables, which are often difficult to capture using traditional statistical methods. Recently, [PERSON]. [10] developed machine learning models utilizing global positioning system (GPS) measurements for the rate of total electron content (TEC) predictions. The authors leverage historical data of solar wind parameters for good scintillation predictions. In addition, Ref. [11] constructed a machine learning model utilizing Global Navigation Satellite Systems (GNSS) for scintillation prediction in high-latitude regions. In the past, several works have reported on the contribution of Pc5 ULF waves to the generation of ionospheric scintillation. For instance, Ref. [12] reported on an F region plasma drift oscillation associated with ULF wave electric field fluctuations. In addition, Ref. [5] reported on ionospheric density and drift perturbations associated with a Pc5 wave event, and Ref. [13] also reported on an electric field oscillation at the equator associated with a Pc5 wave. The results from these authors showed a close correlation between Pc5 ULF waves and ionospheric scintillations, with probable speculation that these waves can drive density fluctuations in the ionosphere. Here, we showed that Pc5 waves can contribute to the excitation of ionospheric scintillations and provide a good way to probe this phenomenon. We developed two machine learning models to predict ionospheric scintillation in Africa at the equatorial anomaly considering the contributions of Pc5 wave power and information from the solar wind parameters. Factoring the contribution of Pc5 wave power in the development of the model provides a good insight into the triggering conditions of the ionospheric scintillation phenomenon and it is crucial due to the good correlation between Pc5 waves and ionospheric scintillations. Machine learning involves creating algorithms to identify patterns in data, allowing for accurate predictions and forecasts [14; 15]. The basic building elements of machine learning models are Artificial Neural Networks (ANN), which can function as a unit when connected in a network [16]. This is considered a mathematical simulation of biological neurons, resembling those found in the human central nervous system.