基于多机器学习竞争策略的短时雷电预报

传统的雷电数据预测方法往往采用单一最优机器学习算法，较少考虑气象数据的时空变化等现象。针对该现象，提出一种基于集成策略的多机器学习短时雷电预报算法。首先，对气象数据进行属性约简，降低数据维度；其次，在数据集上训练多种异构机器学习分类器，并基于预测质量筛选最优基分类器；最后，通过对最优基分类器训练权重，并结合集成策略产生最终分类器。实验表明，该方法优于传统单最优方法，其平均预测准确率提高了9.5%。

Short-term lightning prediction based on multi-machine learning competitive strategy

The traditional lightning data forecasting methods often use single optimal machine learning algorithm to forecast, not considering the spatial and temporal variations of meteorological data. For this phenomenon, an ensemble learning based multi-machine learning model was put forward. Firstly, attribute reduction was conducted for meteorological data to reduce dimension; secondly, multiple heterogeneous machine learning classifiers were trained on data set and optimal base classifier was screened based on predictive quality; finally, the final classifier was generated after weighted training for optimal base classifier by using ensemble strategy. The experimental results show that, compared with the traditional single optimal algorithm, the prediction accuracy of the proposed model is increased by 9.5% on average.