Multiple lagged models, not significant

In this study, the application of Artificial Neural Networks (ANN) and Multiple Regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. The errors of the testing sets for ANN models are generally lower compared to multiple regression models. This PDF file contains the non-significant multiple lagged models for this study. These have been generated in SPSS. The document is 58 pages long and contains 23 regression models, each with the following tables: Variables entered/removed; Model summary; ANOVA; Coefficients; Collinearity diagnostics; and Residuals statistics.

本研究探讨了将人工神经网络（ANN）与多元回归分析（MR）应用于澳大利亚维多利亚州长期季节性春季降水的预测，并利用滞后厄尔尼诺-南方涛动（ENSO）和印度洋偶极子（IOD）作为潜在预测因子。提出使用双重（结合滞后ENSO-IOD）输入集对ANN和MR模型进行校准和验证，以研究这两种主要气候模式过去值对长期春季降水预测的协同效应。选取未违反统计显著性及多重共线性限制的MR模型用于未来春季降水的预测。ANN模型采用多层感知器形式，并使用Levenberg-Marquardt算法进行开发。与多元回归模型相比，ANN模型的测试集误差通常较低。此PDF文件包含本研究的非显著多滞后模型，这些模型已在SPSS中生成。该文档共计58页，包含23个回归模型，每个模型均附有以下表格：变量输入/移除；模型摘要；方差分析；系数；多重共线性诊断；以及残差统计信息。