ERA-40数据库中运用自组织映射技术检测环境变化

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《数据科学杂志》发表了一篇题为"Detecting Environmental Change Using Self-Organizing Map Techniques Applied to the ERA-40 Database"的文章，该研究探讨了在环境变化检测方面，自组织映射（Self-Organizing Map, SOM）技术的有效应用。文章作者包括来自北卡罗来纳州立大学的Mohamed Gebril、Eric Kihn、Eyad Haj Said和Abdollah Homaifar等人，他们在气象学领域展示了数据挖掘工具在处理大量卫星和观测仪器收集的数据中的价值。文章的重点在于介绍如何利用SOM算法，这是一种非线性数据分析方法，它能够对高维度数据进行有效的聚类和可视化，特别适用于分析复杂的时空数据，尤其是在描述气候现象时。研究者将SOM技术应用于欧洲再分析（ERA-40）数据集中的特定区域，旨在探索如何通过这种方法识别和理解气候变化模式。通过这种方法，研究人员能够在庞大的气候数据集中发现潜在的气候趋势、异常事件或区域性的气候响应。SOM的特性使其能够在保持数据空间结构的同时，将复杂的数据简化为二维或三维的可视图，这使得科学家们可以直观地比较不同时期的气候条件，从而更好地理解和预测环境变化。此外，文章可能还涉及到了SOM技术的具体实施步骤，如数据预处理、参数设置、训练过程以及结果的解释和验证。为了确保研究的可靠性，作者可能会讨论如何评估和优化SOM模型的性能，以及与其他传统气候分析方法（如线性回归或时间序列分析）的比较。这篇论文不仅提供了一种创新的环境监测手段，而且也展示了数据科学特别是数据挖掘技术在气象领域的实践应用，对于理解和应对全球气候变化具有重要意义。通过阅读这篇文章，读者可以深入了解如何利用SOM技术进行气候数据分析，并期待在未来的研究中看到更多的此类应用案例。

2 APPROACH

While the analysis of large spatial-temporal datasets can be achieved using different linear and nonlinear methods,

we consider the SOM to be a very promising tool for clustering. It is preferable to other clustering techniques

because the spatial organization of the neurons maps cluster similarity into prototype proximity in the 2D space.

The distance among prototypes in the SOM map can, therefore, be considered as an estimate of the similarity

between objects belonging to clusters. In addition, SOM is an excellent tool for visualizing high dimensional data

and mapping spatial-temporal datasets describing nonlinear phenomena. Figure 1 shows the main stages of our work.

Figure 1.

Main Stage of the proposed approach

2.1 Self-Organizing Map

Due to its high-dimensionality, the data was first analyzed and simplified before proceeding with other analyses

(Comrie & Glen, 1999). SOM is a special case of a neural network that simultaneously summarizes a set of variables

and clusters observations. It can be viewed as a Principal Component Analysis (PCA) combined with a cluster

analysis with both procedures influencing each other in the algorithm. The most commonly used algorithm for

constructing the SOM follows the work of Kohonen (1995). The Kohonen maps are known to be “topology

preserving,” i.e., observations with similar multi-dimensional vectors of variables find themselves positioned close

to each other on the map, and they are also “self-organizing,” i.e., the variables tend to vary along the map in a

meaningful way.

Data clustering in a SOM is the unsupervised classification of patterns (observations, data items, or feature vectors)

into groups (clusters). The input data for the model is described in terms of vectors, each of which consists of

components representing an elementary feature (for instance, sea ice cover, sea surface temperature, etc.) of the data

item, expressed as a numeric value. The output is a similarity-based map of the data items, similarity being defined

as proximity of items in the feature space. The SOM is a neural model in which the output neurons will compete to

be activated or fired. The neuron that wins the competition by having the minimum value using some distance

measurement method (e.g., Euclidean distance) is called the “winning neuron.” In a “winner-takes-all" (WTA)

strategy, only the weights associated with the winner neuron are updated. There is also a winner takes most (WTM)

strategy, where many neurons in the neighborhood of the winner neuron adapt their weights in each learning

iteration (Brocki, 2007).

The lattice is usually one or two-dimensional. Higher dimensional maps are possible but not as common because the

goal of the map is visualization for the applications of this kind of network. Figure 2 shows the structure of the 5x5

lattice in the SOM.

Figure 2. Illustration of the SOM model

Data Science Journal, Volume 10, 8 June 2011

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