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首页> 外文期刊>International Journal of Swarm Intelligence and Evolutionary Computation >Ozone Hole Area Prediction at Earth's North and South Poles by Marvel Interface
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Ozone Hole Area Prediction at Earth's North and South Poles by Marvel Interface

机译：奇迹界面预测地球南北两极的臭氧空洞面积

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This paper explores the possibility of predicting ozone hole area (Maximum Area) at North and South Pole using Artificial Neural Network (ANN) and then developing the forecasting network by using Graphical User Interface (GUI) named MARVEL. Two models are designed for predictions: a) Ozone hole area prediction at North Pole and b) Ozone hole area prediction at South Pole. For both the models, the number of input parameters is taken as year, month, date, sunspot area, sunspot number and solar mean magnetic field. Here, more than 35 years of data is used for training purpose and then predictions are made from November 23, 2015 to September 30, 2016. Forecasting network (MARVEL) is developed to imbibe the properties of ANN. It can get trained with the most recent data accessible to the user and then making future predictions for short (one day) and long term (months, years) durations, respectively. From the results, Mean Square Error (MSE) for Model 1 and Model 2 is found to be 6.7166 DU and 0.3582 DU, respectively. It can be concluded that with 30 numbers of neurons and input and output transfer functions as Tangent Sigmoid and pure linear, along with one hidden layer, the forecasting network predictions are plausible and appreciably close to actual observed values. It is to be noted that the change of ozone hole area at poles have dynamic reasons behind it and sun parameters are not responsible for that. This paper is an attempt to present the application of Artificial Neural Network of connecting the unrelated parameters and processes.
机译：本文探讨了使用人工神经网络（ANN）预测北极和南极臭氧空洞面积（最大面积）的可能性，然后使用名为MARVEL的图形用户界面（GUI）开发预报网络。设计了两种模型进行预测：a）北极的臭氧孔面积预测和b）北极的臭氧孔面积预测。对于这两个模型，输入参数的数量取为年，月，日期，黑子面积，黑子数和太阳平均磁场。在这里，超过35年的数据用于培训目的，然后从2015年11月23日至2016年9月30日进行预测。开发了预测网络（MARVEL）以吸收ANN的属性。可以使用用户可以访问的最新数据进行培训，然后分别对短期（一天）和长期（几个月，几年）进行将来的预测。从结果中，发现模型1和模型2的均方误差（MSE）分别为6.7166 DU和0.3582 DU。可以得出结论，由于具有30个神经元，并且输入和输出传递函数为正切Sigmoid和纯线性，以及一个隐藏层，因此预测网络的预测是合理的，并且与实际观测值相当接近。要注意的是，两极臭氧孔面积的变化背后有动态原因，而太阳参数对此不负责。本文试图提出人工神经网络在连接无关参数和过程中的应用。

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