CAJ | 학술논문

选用叠加马尔科夫链和BP神经网络模型，利用1951年-2013年的降水资料对长春市降水量进行预测。叠加马尔科夫链方法具有原理简单易懂，计算方法简便的优点。BP神经网络模型具有计算快速，占用内存小，还有很好的容错性。根据计算，BP神经网络预测误差均小于20％，叠加马尔科夫链方法仅有6个年份误差小于20％。通过对比分析预测结果可知：对于本研究区BP神经网络模型的预测精度较高。预测方法对数据的稳定性有要求，数据的波动越强，预测结果精度越低。对比可知，叠加马尔科夫链对数据的平稳性要求更高，BP神经网络模型的适用性更强。
선용첩가마이과부련화BP신경망락모형，이용1951년-2013년적강수자료대장춘시강수량진행예측。첩가마이과부련방법구유원리간단역동，계산방법간편적우점。BP신경망락모형구유계산쾌속，점용내존소，환유흔호적용착성。근거계산，BP신경망락예측오차균소우20％，첩가마이과부련방법부유6개년빈오차소우20％。통과대비분석예측결과가지：대우본연구구BP신경망락모형적예측정도교고。예측방법대수거적은정성유요구，수거적파동월강，예측결과정도월저。대비가지，첩가마이과부련대수거적평은성요구경고，BP신경망락모형적괄용성경강。
This paper superimposes Markov chains and BP neural network model to predict rainfall in Changchun City based on the precipitation data from 1951 to 2013 .The method of superimposed Markov chains has the benefits of being easy to understand and to calculate .The BP neural network model can give the result quickly and it's precise and credible .According to calculation ,BP neural network's predictions error are all less than 20% ,in the predictions of superimposed Markov chains ,only six years'prediction errors are less than 20% .By comparing and analyzing the results of prediction ,BP neural network model's prediction accuracy is relatively high for this study area .It can be seen ,prediction methods require the stability of the data .The stronger the fluctuations in the da-ta ,the lower the prediction accuracy .In contrast ,superimposed Markov chain has higher requirements of stability on the data ,and BP neural network model is stronger in applicability .