Thunderstorm frequency (TSF) prediction with higher accuracy is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed two novel hybrid machine learning models through hybridization of data pre-processing ensemble empirical mode decomposition (EEMD) with two state-of-arts models, namely artificial neural network (ANN), support vector machine for TSF prediction at three categories over Bangladesh. We have demarcated the yearly TSF datasets into three categories for the period 1981–2016 recorded at 28 sites; high (March–June), moderate (July–October), and low (November–February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared with classical ANN, SVM, and autoregressive integrated moving average. EEMD-ANN and EEMD-SVM hybrid models showed 8.02–22.48% higher performance precision in terms of root mean square error compared to other models at high-, moderate-, and low-frequency categories. Eleven out of 21 input parameters were selected based on the random forest variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category, and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid models developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world.

在气候极端情况下，更高精度的雷暴频率（TSF）预测对于减少潜在损害具有重要意义。但是，TSF预测很少受到关注，因为雷暴事件是复杂且独特的天气情况的组合，具有很高的不稳定性，因此很难进行预测。为了弥补这一差距，我们通过将数据预处理整体经验模式分解（EEMD）与两个最新模型（即人工神经网络（ANN），支持向量机用于TSF预测）进行杂交，提出了两个新颖的混合机器学习模型。在孟加拉国的三个类别中。我们将1981-2016年期间的年度TSF数据集划分为三类，分别记录在28个站点上。TSF月份较高（3月至6月），中等（7月至10月）和较低（11月至2月）。将拟议的EEMD-ANN和EEMD-SVM混合模型的性能与经典的ANN，SVM和自回归综合移动平均值进行了比较。EEMD-ANN和EEMD-SVM混合模型在均方根误差方面表现出比其他高，中和低频类别的模型高8.02–22.48％的性能精度。基于随机森林变量重要性分析，从21个输入参数中选择了11个。敏感性分析结果表明，每个输入参数对建立每个类别的最佳模型均具有积极作用，而雷暴天数是影响TSF预测的最重要参数。所提出的混合模型优于传统模型，其中EEMD-ANN对于高TSF预测最熟练，而EEMD-SVM对于中等和低TSF预测最熟练。研究结果表明，EEMD与常规模型进行杂交可提高预测精度。在这项工作中开发的混合模型可用于孟加拉国以及世界不同地区的TSF预测。