基于深度學(xué)習(xí)的LAFs短臨強(qiáng)降水預(yù)測(cè)模型研究

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中圖分類號(hào)：P426.6 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：2095-2945(2025)14-0072-04

Abstract:Whenshort-termandimminentrainfallafectsthegenerationofrunoffandthedistributionofwaterresources, accurateforecastingcanbringhugeeconomicbenefitstorelevantdepartments.Inordertoimprovetheacuracyofheavyrainfal forecast，anLAFsshortimminentheavyrainfallforecastmethodbasedonLSTM-Atentioncombinedwiththeaumulatoris proposed.Themodelfirstusesacubicpolynomialinterpolationmethodtogridtheactualobservationelementsoftheground station;Thenthedataisextractedandfusedthroughtheaccumulator;Finaly，theobtainedfeaturefactorsareusedasinputsto themodelformodelprediction.ThreatscoreTSandmeansquareerorareselectedasindicatorstocomprehensivelyevaluatethe performanceof theproposedmodel，andcomparedwithLSTMandConvLSTM.Theresultsshowthattheperformanceof the proposed model is better than the other two models,and its TS score is 2% 業(yè) 3% higher than that of the other two models,and 5% （2 higherthantheactualoperationalforecastlevelinthesameregion,indicatingthattheproposedmodelhascertainpracticalvalue.

Keywords:longshort-termmemorynetwork (LSTM);atentionmechanism;feed-forwardnetwork；short-termimminentheavy precipitation; accumulator

短時(shí)強(qiáng)降水引發(fā)的山洪、城市內(nèi)澇和地質(zhì)災(zāi)害已屢見不鮮，精準(zhǔn)的降水預(yù)報(bào)對(duì)于防洪、水資源管理、空中交管以及能源管理有重要的戰(zhàn)略意義。（剩余8653字）