【摘要】 降雨量的大小会严重影响到一个地区水的质量。基于一个地区的雷达反射率数据和翻斗式雨量计(TB)数据,采用数据挖掘的方法进行降雨量的建模和预测。结合基于TB和基于雷达的降雨量预测模型的优点,提出一种充分利用TB数据和雷达数据进行降雨量预测的新模型。在这种预测模型中采用五种数据挖掘的方法:神经网络、随机森林、分类和回归树、支持向量机和K-最近领域法。为了分析模型的准确性和稳健性,以一种基于历史数据的基准模型和一种基于临近区域TB数据的模型用于对比。通过与几种模型的比较验证了该模型的准确性和有效性。更多还原

【Abstract】 The size of rainfall may severely affect the water quality in a region.In this paper we use data mining approach to model and predict the rainfall based on radar reflectivity data and tipping-bucket( TB) data in a region.By combining the advantages of TB-based and radar-based rainfall prediction models,in the paper we resent a new model which makes full use of TB data and radar reflectivity data to predict rainfall.In such prediction model five data mining algorithms are used: the neural network,the random forest,the classification and regression tree,the support vector machine,and the k-nearest neighbour.To demonstrate the accuracy and robustness of the proposed model,we also consider a historical data-based benchmark model and a neighbouring region TB data-based model for comparison.The accuracy and effectiveness of the proposed model are shown by the comparison between a couple of models.更多还原