RETENCJAPL Sp. z o.o., Gdańsk 80-868, Poland; Faculty of Natural Sciences, University of Silesia in Katowice, Sosnowiec 41-200, Poland.
Faculty of Natural Sciences, University of Silesia in Katowice, Sosnowiec 41-200, Poland.
Sci Total Environ. 2022 Jul 10;829:154588. doi: 10.1016/j.scitotenv.2022.154588. Epub 2022 Mar 16.
Despite growing access to precipitation time series records at a high temporal scale, in hydrology, and particularly urban hydrology, engineers still design and model drainage systems using scenarios of rainfall temporal distributions predefined by means of model hyetographs. This creates the need for the availability of credible statistical methods for the development and verification of already locally applied model hyetographs. The methodology development for identification of similar rainfall models is also important from the point of view of systems controlling stormwater runoff structure in real time, particularly those based on artificial intelligence. This paper presents a complete methodology of division of storm rainfalls sets into rainfalls clusters with similar temporal distributions, allowing for the final identification of local model hyetographs clusters. The methodology is based on cluster analysis, including the hierarchical agglomeration method and k-means clustering. The innovativeness of the postulated methodology involves: the objectivization of clusters determination number based on the analysis of total within sum of squares (wss) and the Caliński and Harabasz Index (CHIndex), verification of the internal coherence and external isolation of clusters based on the bootmean parameter, and the designated clusters profiling. The methodology is demonstrated at a scale of a large urban precipitation field of Kraków city on a total set of 1806 storm rainfalls from 25 rain gauges. The obtained results confirm the usefulness and repeatability of the developed methodology regarding storm rainfall clusters division, and identification of model hyetographs in particular clusters, at a scale of an entire city. The applied methodology can be successfully transferred on a global scale and applied in large urban agglomerations around the world.
尽管在水文科学，尤其是城市水文学领域，人们可以越来越多地获取高时间分辨率的降水时间序列记录，但工程师们仍在使用通过雨型模型预先定义的降雨时间分布场景来设计和模拟排水系统。这就需要能够可靠地开发和验证已在当地应用的模型雨型的统计方法。从实时控制雨水径流结构的系统角度来看，识别相似降雨模型的方法的发展也很重要，特别是基于人工智能的系统。本文提出了一种完整的方法，可将暴雨降雨数据集划分为具有相似时间分布的降雨群，从而最终确定当地模型雨型群。该方法基于聚类分析，包括层次聚类法和 k-均值聚类。所提出方法的创新性在于：基于总平方和（wss）和 Caliński 和 Harabasz 指数（CHIndex）分析，客观确定聚类数量；基于 bootmean 参数验证聚类的内部一致性和外部隔离；指定聚类的特征描述。该方法在一个包括 25 个雨量计的 1806 次暴雨总数据集上，对克拉科夫市大尺度城市降水场进行了演示。所得结果证实了该方法在整个城市范围内对暴雨降雨群的划分以及特定群中模型雨型的识别具有实用性和可重复性。所应用的方法可以成功地在全球范围内推广，并应用于世界各地的大型城市群。
