基于遺傳算法的特征選擇方法在短時(shí)強(qiáng)降水預(yù)報(bào)中的應(yīng)用

doi:10.12306/2021jms.0031

首頁 > 過刊瀏覽>2023年第43卷第1期 >126-134. DOI:10.12306/2021jms.0031

基于遺傳算法的特征選擇方法在短時(shí)強(qiáng)降水預(yù)報(bào)中的應(yīng)用
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                        10.12306/2021jms.0031
                    
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中圖分類號:P457.6
基金項(xiàng)目:北極閣開放研究基金—南京大氣科學(xué)聯(lián)合研究中心(NJCAR2018MS05);國家自然科學(xué)基金資助項(xiàng)目(51977100)

Application of feature selection method based on genetic algorithm in short-term heavy precipitation forecast

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利用江蘇省13個(gè)氣象觀測站歷史上短時(shí)強(qiáng)降水觀測資料,用遺傳算法進(jìn)行特征選擇,選定影響短時(shí)強(qiáng)降水的950 hPa假相當(dāng)位溫、700 hPa比濕、500 hPa比濕、對流有效勢能(Convective Available Potential Energy,CAPE)等14個(gè)特征為主要因素,將是否為短時(shí)強(qiáng)降水抽象成二元分類問題。借助機(jī)器學(xué)習(xí)中CART決策樹算法進(jìn)行分類分析,構(gòu)建便于使用的短時(shí)強(qiáng)降水預(yù)報(bào)規(guī)則集。實(shí)驗(yàn)部分,隨機(jī)選擇5 816條樣本進(jìn)行訓(xùn)練模型,得到適合江蘇地區(qū)的短時(shí)強(qiáng)降水規(guī)則集,利用剩余的1 454條數(shù)據(jù)進(jìn)行實(shí)際檢驗(yàn),模型的短時(shí)強(qiáng)降水預(yù)報(bào)準(zhǔn)確率為91.35%,非強(qiáng)降水預(yù)報(bào)準(zhǔn)確率為97.11%,較特征選擇之前分別提升了8.66%和1.05%。

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Based on the historical observation data of short-term heavy precipitation at 13 meteorological stations in Jiangsu Province, the genetic algorithm was used for feature selection, and 14 features, such as pseudo equivalent potential temperature at 950 hPa, specific humidity at 700 hPa, specific humidity at 500 hPa and Convective Available Potential Energy(CAPE), were selected as the main factors to abstract the short-term heavy precipitation into a binary classification problem. With the help of CART algorithm in machine learning, a set of short time heavy precipitation forecast rules for the use of forecasters is constructed. In the experiment part, 5 816 samples were randomly selected to train the model, and the rule set of short-time heavy rainfall suitable for Jiangsu was obtained. The remaining 1 454 data were used for practical test. The prediction accuracy of short-time heavy precipitation was 91.35% and that of non-heavy precipitation was 97.11%, which are 8.66% and 1.05% higher than that before feature selection.

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耿煥同,戴中斌,沈陽.基于遺傳算法的特征選擇方法在短時(shí)強(qiáng)降水預(yù)報(bào)中的應(yīng)用.氣象科學(xué),2023,43(1):126-134 GENG Huantong, DAI Zhongbin, SHEN Yang. Application of feature selection method based on genetic algorithm in short-term heavy precipitation forecast. Journal of the Meteorological Sciences,2023,43(1):126-134

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收稿日期:2020-10-27
最后修改日期:2021-03-01
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在線發(fā)布日期: 2023-05-15
出版日期: 2023-02-15

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