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首页> 外文期刊>Journal of Hydroinformatics >Data-mining approach to investigate sedimentation features in combined sewer overflows
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Data-mining approach to investigate sedimentation features in combined sewer overflows

机译：数据挖掘方法研究下水道溢流的沉积特征

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Sedimentation is the most common and effectively practiced method of urban drainage control in terms of operating installations and duration of service. Assessing the percentage of suspended solids removed after a given detention time is essential for both design and management purposes. In previous experimental studies by some of the authors, the expression of iso-removal curves (i.e. representing the water depth where a given percentage of suspended solids is removed after a given detention time in a sedimentation column) has been demonstrated to depend on two parameters which describe particle settling velocity and flocculation factor. This study proposes an investigation of the influence of some hydrological and pollutant aggregate information of the sampled events on both parameters. The Multi-Objective (EPR-MOGA) and Multi-Case Strategy (MCS-EPR) variants of the Evolutionary Polynomial Regression (EPR) are originally used as data-mining strategies. Results are proved to be consistent with previous findings in the field and some indications are drawn for relevant practical applicability and future studies.
机译：就运营设施和服务期限而言，沉淀是城市排水控制中最常用和最有效的方法。对于设计和管理目的而言，评估在给定的滞留时间后去除的悬浮固体的百分比至关重要。在一些作者先前的实验研究中，等值去除曲线的表达（即代表水深，其中在给定的滞留时间后在沉淀塔中去除了一定百分比的悬浮固体）取决于两个参数描述颗粒沉降速度和絮凝因子。这项研究建议对一些采样事件的水文和污染物总量信息对这两个参数的影响进行调查。进化多项式回归（EPR）的多目标（EPR-MOGA）和多案例策略（MCS-EPR）变体最初用作数据挖掘策略。结果被证明与该领域的先前发现一致，并且为相关的实际适用性和将来的研究指明了一些迹象。

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