组合模型在降雨量预测中的应用

研究降雨量准确预测问题,降水量的变化既受大气环流、地形、气压、气候带等各种环境因子的影响,降水量的动态特征呈现复杂非线性和各种干扰因素,预测不可能准确。传统预测模型难以对其进行准确预测,预测精度低。为提高降雨量的预测精度,提出一种组合模型的降雨量预测模型。首先采用小波分析将降雨量数据进行分解成线性和非线性部分,然后分别采用ARIMA和RBF神经网络模型对其进行预测,最后采用小波重构线性和非线性预测结果,得到降雨量最终预测结果。仿真结果表明,相对于传统预测模型,组合模型提高了降雨量预测精度,预测结果可以帮助农业、水利部门提高防治旱涝灾害的科学依据。

Application of Combination Model in Rainfall Prediction

Study the prediction accuracy of rainfall.In order to improve the prediction accuracy of rainfall,this paper presented a combination rainfall prediction model.Firstly,the rainfall data were decomposed into linear and nonlinear parts by using the wavelet analysis,and then the ARIMA and RBF neural network models were used to predict the two parts.Finally,the linear and nonlinear prediction results were reconstructed by wavelet analysis to get the final forecasting result of rainfall.The simulation results show that the combination model improves the prediction accuracy of rainfall compared with the traditional prediction model,and the prediction results can improve the prevention of flood and drought disaster in agriculture and water conservancy.