用SST异常作为预报器的印度冬季季风降雨的多变量预报：神经计算和统计方法
Multivariate forecast of winter monsoon rainfall in India using SST anomaly as a predictor: Neurocomputing and statistical approaches
摘要：
本文在分析了现有文献的基础上，冬季风期间降水与海表温度异常之间关系的复杂性（11月至1月）用散点图矩阵和自相关函数对印度上空的大气环流进行了统计评价，得到了线性和多项式趋势方程，观察到线性趋势的决定系数很低，即使当多项式趋势使用了6度。利用前一年冬季风月份的降水量和海表温度距平作为预报因子，建立了指数回归方程和具有广泛变量选择的人工神经网络，对给定年份的冬季风平均降水量进行了预报。使用Levenberg-Marquardt算法生成多元指数回归方程的回归系数。人工神经网络的形式是一个多层感知器，具有S形非线性和基于遗传算法的变量选择。使用Willmott指数、预测误差百分比和预测产量对两种预测模型进行统计学判断。统计评估揭示了人工神经网络在指数回归上的潜力。
英文摘要：
In this paper, the complexities in the relationship between rainfall and sea surface temperature (SST) anomalies during the winter monsoon (November-January) over India were evaluated statistically using scatter plot matrices and autocorrelation functions.Linear as well as polynomial trend equations were obtained and it was observed that the coefficient of determination for the linear trend was very low and it remained low even when polynomial trend of degree six was used. An exponential regression equation and an artificial neural network with extensive variable selection were generated to forecast the average winter monsoon rainfall of a given year using the rainfall amounts and the sea surface temperature anomalies in the winter monsoon months of the previous year as predictors. The regression coefficients for the multiple exponential regression equation were generated using Levenberg-Marquardt algorithm. The artificial neural network was generated in the form of a multiplayer perceptron with sigmoid non-linearity and genetic-algorithm based variable selection. Both of the predictive models were judged statistically using the Willmott index, percentage error of prediction, and prediction yields. The statistical assessment revealed the potential of artificial neural network over exponential regression.
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