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首页> 外文期刊>Pure and Applied Geophysics >Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis
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Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

机译：基于主成分分析的人工神经网络对臭氧总月浓度的建模与预测

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摘要

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34′10. 92″N, 88°22′10. 92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.
机译：在本文讨论的工作中，我们考虑了印度东部城市地区加尔各答（22°34′10.92″ N，88°22′10.92″ E）的总臭氧时间序列。我们使用云量，平均温度和降雨量作为预测指标，开发了一种人工神经网络，以具有S形非线性的多层感知器形式，用于根据前几个月的预测指标值预测每月总臭氧浓度。我们还根据当月的预测值估算了臭氧总量。在开发神经网络模型之前，我们通过主成分分析消除了多重共线性。基于主成分分析提取的变量，我们开发了三种人工神经网络模型。通过严格的统计评估，发现当将云量和降雨量作为多层感知器形式构建的神经网络模型的输入变量集时，它们可以作为每月总臭氧的良好预测指标。通常，基于气象预报器，人工神经网络具有很好的潜力来预测和估算每月的总臭氧量。进一步观察到，在季风前和冬季，拟议的模型表现优于季风前后。

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