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About global warming

2020-04-11 来源: 51Due教员组 类别: Report范文

大家好 今天带来一篇关于全球气候的report 由于全球气候变化,许多地区的海平面多年来一直在上升。从之前构建的访谈中,我们可以发现全球变暖是造成这一现象的关键因素。然而,如何挖掘这一现象背后的日期对我们来说似乎很重要,因为许多有意义的发现可以从日期中获得。因此,有必要对如何用有效的方法挖掘数据进行研究。 下面一起来看看

 

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In this part, we mainly discuss the research methods of obtaining sea level data and prediction in Shanghai area.

As we all know, sea levels in many areas have been rising for many years as a result of global climate change. From previous interviews, we can see that global warming is a key factor in this phenomenon. However, it seems important to us how we dig for the dates behind this phenomenon, as many meaningful discoveries can be made from the dates. Therefore, it is necessary to study how to mine data with effective methods.

Before applying detailed research methods to analyze the data, relevant data needs to be collected first. For sea level in Shanghai area, in order to obtain more accurate results, some types of data are needed, such as elevation data, submarine topography data, tidal station data, ground subsidence data, etc. You can do this by looking at the relevant databases Shared on the site. After obtaining these data, the next step is to use SPSS, Matlab, Vensim, Arcgis and other analysis software for preprocessing. In this case, some unreal data may be removed from the original data source. Then, some research methods can be used to mine the information behind the processing data.

Many research methods have been proposed in sea level analysis. First is the wavelet transform method which has attracted much attention in recent years. It comes from functional analysis, Fourier analysis, spline analysis and harmonic analysis. Wang (2013) used the wavelet transform method to analyze the sea level change in Japanese waters. Based on his research in the paper, the wavelet transform method is mainly used to eliminate some negative effects caused by partial loss of related data. In this case, the analysis of sea level changes in Japanese waters is likely to be more accurate. The second method is the winters exponential smoothing method of time series. The method is nonlinear in the estimation of data, and its goal is to minimize the mean square error between the predicted value and the exact value. In addition, the method can also be used to deal with the data in time series varying with seasonal factors. Wang (2013) used the winter exponential smoothing method to obtain the prediction of sea level change in Shanghai from 2007 to 2015. In his study, he found that sea levels would rise by 140-150 millimeters from 2006 levels. Figure 1 depicts the predicted results of the winters exponential smoothing method (Wang, 2013).

The third method is the classical spectral estimation method, which belongs to spectral analysis. It can be divided into direct method and indirect method. In the direct method, the Fourier transform is applied to the sample to obtain the power spectrum of the data. In the indirect method, the autocorrelation estimation between samples is first performed. Then the power spectrum of the data is obtained by Fourier transform. Duan et al. (2014) used the tidal data of tanggu and the spectral estimation method to predict sea level changes. The wavelet transform method and spectrum estimation method are combined to make up for the shortcomings of the two methods and improve the prediction accuracy. In addition to the above methods, the specific volume of seawater is usually calculated by the method of specific volume height. Tabata et al. (1986) first established a model for calculating specific volumes. Chen (2009) used this method to calculate the temperature space component of the north Pacific Ocean. See figure 2.

Based on the introduction of relevant research methods, we can find that many different methods can be used to predict the change of sea level. Obviously, these methods have some inherent flaws and limitations. Therefore, a mixture of different methods is often used, such as the method proposed by Duan et al.(2014) above.

The method used in this report can be applied to the prediction of sea level change in Shanghai area due to its convenience and high computational efficiency. The data was collected through a number of Shared databases, including the China oceanic administration, the Shanghai municipal bureau of water resources and a number of Shared global climate databases. After the expected data is obtained, the following sections of this report deal with the results of the data processing.


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