Research graph
References from Predicting debris criticality in urban sewers: Survival and artificial intelligence approaches. Local targets link to admitted publications; unresolved targets remain external evidence.
Markov chain modulated poisson process to stimulate the number of blockages in sewer networks [Article]
10.1139/cjce-2018-0104 · 2019 · External reference
Machine learning approach to root intrusion prediction in urban sewers using CCTV and environmental features
10.14455/isec.2025.12(1).env-03 · 2025 · External reference
Analysis of drainage system defects using co-occurrence patterns
10.1016/j.tust.2026.107708 · 2026 · External reference
Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany [Article]
10.2166/hydro.2018.217 · 2018 · External reference
Rethinking modelling of particulate pollutants in combined sewer overflows (CSOs): a focus on model structure [Article]
10.1016/j.jhydrol.2025.133239 · 2025 · External reference
Determining relative weights of sewer pipelines’ components and defects
10.1061/(asce)ps.1949-1204.0000290 · 2018 · External reference
Machine learning-assisted optimal schedule of underground water pipe inspection [Article]
10.1186/s43065-023-00086-5 · 2023 · External reference
A two-stage data-driven spatiotemporal analysis to predict failure risk of urban sewer systems leveraging machine learning algorithms [Article]
10.1111/risa.13742 · 2021 · External reference
Sewer sediment deposition prediction using a two-stage machine learning solution [Article]
10.2166/hydro.2024.144 · 2024 · External reference
Analysis of sedimentation mechanisms in small diameter gravity sewers based on computational fluid dynamics
10.1021/acsestwater.4c01036 · 2025 · External reference
Analysis of sewer blockage causes using open data
10.2166/wpt.2024.218 · 2024 · External reference
Forecasting particle froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches [Article]
10.1016/j.rineng.2024.102320 · 2024 · External reference
Sewer life span prediction: comparison of methods and assessment of the sample impact on the results [Article]
2019 · External reference
Predicting the structural condition of sewer pipes: a comparative analysis of random forest and logistic regression models
10.1080/1573062x.2025.2571908 · 2026 · External reference
Risk-based prioritization of sewer pipe inspection from infrastructure asset management perspective
10.3390/su13137213 · 2021 · External reference
Sewer pipe defect detection via deep learning with local and global feature fusion [Article]
10.1016/j.autcon.2021.103823 · 2021 · External reference
Environmental drivers of sediment accumulation in urban sewer systems: a case study of Kyoto City, Japan
2025 · External reference
Combining a statistical model with machine learning to predict groundwater flooding (or infiltration) into sewer networks [Article]
10.1016/j.jhydrol.2021.126916 · 2021 · External reference
Sewer pipes condition prediction models: a state-of-the-art review
10.3390/infrastructures4040064 · 2019 · External reference
Sedimentation in heavy laden urban combined sewer conduits using a multi-phase eulerian-eulerian model
10.1080/1573062x.2023.2229289 · 2023 · External reference
Advancing sensor network optimization and data analytics for smart wastewater and stormwater collection systems: an overview
2025 · External reference
Classification of defects in sewer pipes using neural networks [Article]
10.1061/(asce)1076-0342(2000)6:3(97) · 2000 · External reference
Comparison of machine learning techniques for condition assessment of sewer network [Article]
10.1109/access.2022.3222823 · 2022 · External reference
Spatial heterogeneity assessment of factors affecting sewer pipe blockages and predictions [Article]
10.1016/j.watres.2021.116934 · 2021 · External reference
Sediment level prediction of a combined sewer system using spatial features [Article]
2021 · External reference
Smart management of combined sewer overflows: from an ancient technology to artificial intelligence [Article]
2023 · External reference
Transferability and comparability of sewer deterioration models–a case study on Norwegian sewer data [Article]
10.1080/1573062x.2024.2423391 · 2024 · External reference
Risk assessment of sewer condition using artificial intelligence tools: application to the SANEST sewer system [Article]
10.2166/wst.2013.758 · 2014 · External reference
Development of sustainable water infrastructure: a proper understanding of water pipe failure
10.1016/j.jclepro.2023.136653 · 2023 · External reference
Automatic detection of sewer defects based on improved you only look once algorithm [Article]
10.1016/j.autcon.2021.103912 · 2021 · External reference
Defect-level condition assessment of sewer pipes
10.1061/jwrmd5.wreng-6225 · 2025 · External reference
Hydraulic deterioration models for storm-water drainage pipes: ordered probit versus probabilistic neural network [Article]
10.1061/(asce)cp.1943-5487.0000020 · 2010 · External reference
Survival analysis framework for sewer failure time: evidence from Hong Kong
10.1038/s41545-025-00479-x · 2025 · External reference
When population science meets urban sewer networks: decoding remaining life using life table analytics
2026 · External reference
Forecasting and optimization for minimizing combined sewer overflows using machine learning frameworks and its inversion techniques [Article]
10.1016/j.jhydrol.2023.130515 · 2024 · External reference
Deep learning for automated encrustation detection in sewer inspection
2024 · External reference
A risk assessment model of a sewer pipeline in an underground utility tunnel based on a Bayesian network
10.1016/j.tust.2020.103473 · 2020 · External reference
