崇陽(yáng)溪流域PRBP神經(jīng)網(wǎng)絡(luò)洪水預(yù)報(bào)模型研究

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SI Qi'， JIN Baoming1*，LU Wangming2， CHEN Zhaoqingl （1.ColegeofCiviEineinguzouUvesityuzo8，Ca;2.aningFlooddroughtisastereveiote Nanping ， China）

Abstract：ThePoak-Ribiereconjugategradientbackpropagationalgorithm（PRBP）ofnumericaloptimizationtechnologywasused， and21rainstormandfloodprocessesfrom1997to2O2 intheupperreachesofChongyangRiverbasinwerestudied.Therainfal volumeofsixrainfalltationsintheupperreachesofChongyangRiverbasinandthepreviousdischargeof Wuyishan Hydrological Stationwereregardedasinput，anditscorespondingdischargewasregardedasoutput;thenumberofhiddenlayerunitswas determinedbytrialcalculation，andthenPRBPneuralnetworkfloodforecastingmodelofChongyangxiRiver Basinwasestablished. Theremainingeightfloodswereusedtotestandvalidatethemodel.TheresultsshowthatcomparedwiththatoftheconventionalBP neuralnetworkodel，tonergencespdofeodelissterndthalculationspeedisviouslyiproed;thteistic coefficient of the model is greater than O.87，and the relative error of peak flowof six floods is within 10% . The forecasting accuracy meets the requirements，which can provide a basis forthe flood control department to forecast the flood.

Keywords：PR conjugate gradient method; BP neural network; flood forecasting; Chongyang River Basin

山區(qū)流域洪水往往具有破壞性強(qiáng)、預(yù)見(jiàn)期短、預(yù)報(bào)難度大的特點(diǎn)。（剩余8880字）