评估和提高 K-Means 聚类算法在年度海岸床演变应用中的性能

IF 2.6 3区 地球科学 Q2 OCEANOGRAPHY
Andreas Papadimitriou, Vasiliki Tsoukala
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引用次数: 0

摘要

利用基于过程的模式预测沿岸海床的年尺度演变通常是一项复杂的任务,需要大量的 计算资源。为了弥补这一不足,通常采用旨在减少输入参数量的加速技术。在本研究框架内,对广泛使用的 K-Means 聚类算法的能力进行了全面评估,该算法是一种获取代表性波浪条件的方法。为了改进模型结果,对算法的各种改进进行了研究。利用 MIKE21 耦合模型 FM,利用波浪特征的年度数据集,在希腊 Rethymno 港附近的沙质海岸线上进行了测试。通过将模拟结果与 "蛮力 "模拟结果进行比较,对模型性能进行了评估,"蛮力 "模拟结果包含了由每小时变化的近海海况波浪特征的年度时间序列引起的海床水平面变化,结果非常令人满意。结果发现,性能最好的配置与采用过滤方法从数据集中剔除低能量海况有关。利用 "智能 "配置的聚类算法可以提高其性能,这对于希望获得年度海床水位演变的准确表征,同时减少所需计算工作量的工程师来说,是一个非常有价值的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating and enhancing the performance of the K-Means clustering algorithm for annual coastal bed evolution applications

The prediction of the coastal bed evolution at an annual scale utilizing process-based models is usually a complex task requiring significant computational resources. To compensate for this, accelerating techniques aiming at reducing the amount of input parameters are often employed. In the framework of this research, a comprehensive evaluation of the capacity of the widely-used K-Means clustering algorithm as a method to obtain representative wave conditions was undertaken. Various enhancements to the algorithm were examined in order to improve model results. The examined tests were implemented in the sandy coastline adjacent to the port of Rethymno, Greece, utilizing an annual dataset of wave characteristics using the model MIKE21 Coupled Model FM. Model performance evaluation was carried out for each test simulation by comparing results to a “brute force” one, containing the bed level changes induced from the annual time series of hourly changing offshore sea state wave characteristics, deeming the results very satisfactory. The best-performing configurations were found to be related to the implementation of a filtering methodology to eliminate low-energy sea states from the dataset. Employment of clustering algorithms utilizing “smart” configurations to improve their performance could become a valuable tool for engineers desiring to obtain an accurate representation of annual bed level evolution, while simultaneously reducing the required computational effort.

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来源期刊
Oceanologia
Oceanologia 地学-海洋学
CiteScore
5.30
自引率
6.90%
发文量
63
审稿时长
146 days
期刊介绍: Oceanologia is an international journal that publishes results of original research in the field of marine sciences with emphasis on the European seas.
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