Application of artificial neural networks to rainfall forecasting in Queensland, Australia
Application of artificial neural networks to rainfall forecasting in Queensland, Australia
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DOI：
10.1007/s00376-012-1259-9
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发表时间：
2012-07-01
影响因子：
5.8
通讯作者：
Marohasy, Jennifer
中科院分区：
文献类型：
--
作者：
Abbot, John;Marohasy, Jennifer
在这项研究中，通过将公认的气候指数、每月历史降雨数据和大气温度输入到原型独立、动态、循环、时滞人工神经网络中，评估了人工智能在澳大利亚昆士兰州每月和季节性降雨预测中的应用。使用时间序列图、均方根误差 (RMSE) 和皮尔逊相关系数将输出作为 1993 年至 2009 年期间提前 3 个月的每月降雨量预测与观测到的降雨量数据进行比较。将 RMSE 值与澳大利亚气象局澳大利亚预测海洋大气模型 (POAMA)-1.5 大气环流模型 (GCM) 生成的预测进行比较表明，该原型在 17 个站点中的 16 个站点中实现了较低的 RMSE。综述了人工神经网络在降雨预报中的应用。原型设计被认为是初步的，具有显着改进的潜力，例如包含 GCM 的输出和其他输入属性的实验。
In this study, the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland, Australia, was assessed by inputting recognized climate indices, monthly historical rainfall data, and atmospheric temperatures into a prototype stand-alone, dynamic, recurrent, time-delay, artificial neural network. Outputs, as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009, were compared with observed rainfall data using time-series plots, root mean squared error (RMSE), and Pearson correlation coefficients. A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology's Predictive Ocean Atmosphere Model for Australia (POAMA)-1.5 general circulation model (GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared. The application of artificial neural networks to rainfall forecasting was reviewed. The prototype design is considered preliminary, with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.