機(jī)器學(xué)習(xí)算法對黃河上游人工增雨效果檢驗的應(yīng)用

doi:10.12306/2021jms.0098

首頁 > 過刊瀏覽>2024年第44卷第6期 >1154-1162. DOI:10.12306/2021jms.0098

機(jī)器學(xué)習(xí)算法對黃河上游人工增雨效果檢驗的應(yīng)用
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                        10.12306/2021jms.0098
                    
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中圖分類號:P48
基金項目:第二次青藏高原綜合科學(xué)考察研究項目(2019QZKK0104);國家自然科學(xué)基金資助項目(42165008);西北區(qū)域人影建設(shè)研究項目(RYSY201903);青海省自然科學(xué)基金資助項目(2021-ZJ-745)

Application of machine learning algorithm in the effect verification of artificial rainfall on the upper reaches of the Yellow River

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利用1976—2020年黃河上游8個國家站實測降水?dāng)?shù)據(jù)結(jié)合74項環(huán)流指數(shù),應(yīng)用RF和BP神經(jīng)網(wǎng)絡(luò)算法,建立了分降水等級的月降水等級和日降水等級預(yù)測模型,并檢驗兩種模型對實際降水的預(yù)測效果和模型的穩(wěn)定程度,最終選取合適的預(yù)測模型估計自然降水量并結(jié)合實際增雨作業(yè)和實際降水量檢驗作業(yè)效果。結(jié)果表明:兩種算法的月、日降水等級模型模擬結(jié)果均較好,樣本占總樣本的75%以內(nèi)時模型準(zhǔn)確率達(dá)80%,但對偏極端的月降水量預(yù)測結(jié)果較差。RF算法建立的模型更為穩(wěn)定,在非線性作用和缺失上均有較好的包容性,而BP神經(jīng)網(wǎng)絡(luò)建立的日降水模型中預(yù)測期的誤差大于訓(xùn)練期,模型的穩(wěn)定性相對較差。對2019—2020年甘德縣10次火箭增雨作業(yè)的效果進(jìn)行檢驗,2020年9月18日作業(yè)兩種算法均預(yù)測該日降水為1級,但實際降水等級為2級,認(rèn)為作業(yè)效果有效。

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Based on the precipitation data from 8 national stations in the upper Yellow River from 1976 to 2020 combined with 74 circulation indexes, the random forest algorithm and the BP neural network algorithm were used to establish the monthly precipitation grade and daily precipitation grade prediction models, and then the prediction effect of the two models on actual precipitation as well as the stability of the model were tested. The influence of precipitation was then tested by combining the actual precipitation and the predicted precipitation, and finally an appropriate prediction model was chosen to estimate natural precipitation. Results show that the two algorithms perform better in simulating monthly and daily precipitation, with model accuracy reaching 80% when samples account for less than 75% of the total samples. However, the results for the prediction of extreme monthly precipitation are subpar. The daily precipitation model created by the BP neural network has an error in the prediction period that is higher than that in the training period, and the model's stability is relatively poor. In contrast, the model created by the RF algorithm was more stable and has good tolerance for nonlinear effects and omissions. In Gande County, from 2019 to 2020, the impact of ten rocket operations to increase rainfall was examined. The two algorithms both projected level 1 precipitation for September 18, 2020, however level 2 precipitation actually fell on that day. The effectiveness of the artificial rainfall enhancement operation is thought to be good.

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張玉欣,康曉燕,侯永慧,薛麗梅,田建兵.機(jī)器學(xué)習(xí)算法對黃河上游人工增雨效果檢驗的應(yīng)用.氣象科學(xué),2024,44(6):1154-1162 ZHANG Yuxin, KANG Xiaoyan, HOU Yonghui, XUE Limei, TIAN Jianbing. Application of machine learning algorithm in the effect verification of artificial rainfall on the upper reaches of the Yellow River. Journal of the Meteorological Sciences,2024,44(6):1154-1162

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收稿日期:2021-10-18
最后修改日期:2021-11-11
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在線發(fā)布日期: 2025-01-08
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