Lightning can be described as a high voltage electrical discharge event that occurs between the cloud and the ground. The development of a warning system that enables us to predict lightning before it occurs and to take safety precautions is essential to minimize undesired lightning-related events. This study aims to predict lightning events 1 h in advance by using meteorological data recorded by a single meteorological station. The data used in this study consist of a total of ten atmospheric features, and lightning strikes were examined in three groups by considering distances from the station as 0–2 km (data group-1, DG-1), 2–4 km (DG-2), and 4–6 km (DG-3). The sequential forward selection (SFS) method was used to determine the most effective among the ten features. The probability of lightning for each data group was then estimated with the artificial neural networks algorithm. As a result, we found that the best lightning estimation rate among the three groups was obtained at 0–2 km distance, with 90% accuracy with only four effective features determined by the SFS method. As far as the authors are aware, a detailed analysis of the data and a distance-based lightning prediction system in the literature does not exist, and therefore, the originality and success of this study are expected to contribute to the literature for future studies.

闪电可以描述为发生在云层和地面之间的高压放电事件。开发预警系统，使我们能够在闪电发生之前对其进行预测并采取安全预防措施，对于最大限度地减少与闪电有关的不良事件至关重要。本研究旨在通过使用单个气象站记录的气象数据提前1小时预测雷电事件。本研究中使用的数据总共包括十个大气特征，并且通过将距站点的距离视为0–2 km（数据组1，DG-1），2-4 km（数据组）来对三组雷电进行了检查。 DG-2）和4–6 km（DG-3）。顺序向前选择（SFS）方法用于确定十个功能中最有效的功能。然后，使用人工神经网络算法估算每个数据组的闪电概率。结果，我们发现，三组中的最佳雷电估计率是在0–2 km的距离上获得的，准确度为90％，而通过SFS方法确定的只有四个有效特征。据作者所知，文献中不存在对数据的详细分析和基于距离的雷电预测系统，因此，这项研究的独创性和成功有望为将来的研究提供文献。