Characterizing annual flood patterns variation using information and complexity indices
Characterizing annual flood patterns variation using information and complexity indices
复制标题
使用信息和复杂性指数表征年度洪水模式变化
DOI：
10.1016/j.scitotenv.2021.151382
复制
发表时间：
2022
影响因子：
9.8
通讯作者：
Xiao Cong
中科院分区：
文献类型：
--
作者：
Al Sawaf Mohamad Basel;Kawanisi Kiyosi;Xiao Cong
近几十年来，暴雨与各种复杂的洪水模式相结合的频率增加。由于洪水是多变和不可预测的，更深入地了解洪水事件是必要的，更好的流域管理。本研究的主要目的是调查和表征嵌入在位于日本西部的河流流域的年度洪水模式。为了实现这一目标，我们提出了一种基于信息和复杂性原理的方法来检查嵌入在河流系统中的洪水模式的年变化。所提出的方法的关键优势建立在两个基本支柱上：（1）单词模式;给出关于检测到的洪水模式的图像，即，简单洪水在一天内发生或严重洪水持续两天或更长时间，以及（2）报告检测到的模式的频率和随机性的信息复杂性指数。信息内容量化使用平均信息增益（ESTA），而有效测量复杂性（EMC）和波动复杂性（FC）的指标，用于定义研究记录的复杂性。实验结果表明，该方法在检测隐藏模式方面是非常有效的。此外，我们成功地捕获了表现出相同的洪水模式的站。这一方法第一支柱的主要发现表明，洪水事件基本上是由东亚季风和热带气旋期间发生的降水引发的。或者，信息复杂性支柱是一个强大的工具，在捕捉不同的内部结构的洪水模式。因此，较高的MIG值表示较高程度的随机性。另一方面，较高的EMC值反映了洪水事件的长度，而较高的FC值显示了较高的独立洪水事件的数量。总的来说，本研究讨论了一种新方法的能力，该方法能够捕获数据集中的隐藏模式，并可以扩展到许多调查系统行为的应用程序。
In recent decades, the frequency of torrential rain coupled with various complex flood patterns increased. Since floods are changeable and unpredictable, deeper understanding of flood incidents is necessary for better watershed management. The primary objective of this research is to investigate and characterize annual flood patterns embedded in a river catchment located in west of Japan. To fulfill this aim, we proposed a method based on information and complexity principles to examine the annual variation of flood patterns embedded in a riverine system. The key strength of the proposed approach is being established on two fundamental pillars: (1) word pattern; gives an image about the detected flood patterns, i.e., simple flood occurs within one day or severe flood persists for two days or more, and (2) information-complexity indices that report the frequency and randomness of the detected patterns. Information content was quantified using Mean Information Gain (MIG), whereas Effective Measure Complexity (EMC) and Fluctuation Complexity (FC) were indices used to define the complexity in the studied records. The results show that the proposed method is very powerful in detecting hidden patterns. Furthermore, we succeed in capturing the stations that exhibited the same flood patterns. The main finding of the first pillar of this approach revealed that flood events were fundamentally triggered by precipitation occurred during East Asian monsoon and tropical cyclones. Alternatively, the information-complexity pillar was a powerful tool in capturing different internal structures of flood patterns. Hence, higherMIGvalues indicated higher degree of randomness. On the other hand, higherEMCvalues reflected the length of flood events, while higherFCvalues showed higher number of separated flood events. Overall, this study discusses the competence of a new approach capable to capture hidden patterns in dataset and can be extended to numerous applications that investigate system behavior.