Hybrid knowledge and data driven approach for prioritizing sewer sediment cleaning

Abstract

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.