The efficient assessment of sewer sediment condition is important for municipalities to formulate prioritization strategies for cleaning initiatives. However, manual assessment methods are plagued by inherent subjective and inaccuracy. To address these deficiencies, this paper introduces a hybrid approach integrating both knowledge-based principles and data-driven techniques for Sewer Sediment Cleaning Priority Assessment (SSCPA). The proposed approach exhibits a notable level of assessment accuracy, achieving macro-average precision, recall, and F1-score metrics of 87.9%, 88.0%, and 88.0%, respectively. These findings underscore the efficacy of SSCPA as a valuable tool for evaluating sewer sediment conditions, thereby enhancing the decision-making process for cleaning prioritization efforts. Future research should incorporate the probability of failure as a pivotal factor and explore the temporal dynamics of sewer sediment for more comprehensive insight.

中文翻译：

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用于优先处理下水道沉积物清理的混合知识和数据驱动方法


对下水道沉积物状况的有效评估对于市政当局制定清洁举措的优先策略非常重要。然而，人工评估方法存在固有的主观性和不准确性的问题。为了解决这些缺陷，本文介绍了一种结合基于知识的原则和数据驱动技术的混合方法，用于下水道沉积物清理优先级评估（SSCPA）。所提出的方法表现出显着的评估准确性水平，宏观平均精度、召回率和 F1 分数指标分别达到 87.9%、88.0% 和 88.0%。这些发现强调了 SSCPA 作为评估下水道沉积物状况的宝贵工具的功效，从而增强了清洁优先工作的决策过程。未来的研究应将失败概率作为关键因素，并探索下水道沉积物的时间动态，以获得更全面的见解。