基于云推理的年降水量预测

预测未来年的降水量是对旱情做出预警并采取合理抗旱措施的重要前提,但如何获得准确的预测值一直是研究的热点。降水随时间的变化通常具有随机性和模糊性,而云模型是在传统模糊数学和概率统计的基础上建立起来的,能够实现不确定概念与定量数值之间的自然转化,通过年降水量历史数据及当前趋势挖掘并制定出相应规则,从而推理获得未来年份的降水量。在此基础上,提出了基于小波消噪预处理和理论频率曲线修正的云推理预测模型,并将其运用到了西安市年降水量的预测当中。从所得结果来看,降水量预测值能较好的反映其年际变化规律,模型预测精度得到了很大提高,基本可将误差控制在30%以内。

Prediction of Annual Precipitation Based on Cloud Reasoning Model

Annual precipitation forecast is necessary for drought early warning and taking effective measures to defend drought happen, but how to get more accurate predictive information is still the hotspot. As we known, precipitation often contains randomness interferential with the change of time, while cloud model could realize the transformation between uncertain concept and quantitative values, which is built up on the basis of traditional fuzzy mathematics and probability statistics, thus the volume of precipitation in the future can be achieved through mining association rules from historical and current data. From this, this paper put forward an improved cloud reasoning model to predict annual precipitation which is based on the pretreatment of wavelet de-noise technique and revise of theoretical frequency curve. After that, the predictive results can reflect inter-annual variation of precipitation better in Xi''an, and its accuracy has been greatly improved, the error can be maintained basically less than 30%.