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Water level estimation in sewage pipes using texture-based methods and machine learning algorithms.

K Bhase1, J Myrans2, R Everson3

  • 1South West Water, Peninsula House, Peninsula Park, Rydon Lane, Exeter EX2 7HR, UK

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|March 29, 2025
PubMed
Summary
This summary is machine-generated.

Automated sewage pipe water level estimation improves accuracy over manual methods. Using segmentation masks with Extra Trees regressor provides reliable data for pipe maintenance and rehabilitation planning.

Keywords:
computer visionconvolutional neural networksegmentation modelssewage pipeswater level estimation

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Area of Science:

  • Civil Engineering
  • Environmental Engineering
  • Computer Vision

Background:

  • Water companies utilize closed-circuit television (CCTV) for sewage pipe inspections.
  • Manual water level recording is crucial for assessing pipe condition but prone to inaccuracies.
  • Inaccurate water level data hinders effective sewage pipe maintenance and rehabilitation planning.

Purpose of the Study:

  • To compare six automated methods for estimating water levels in sewage pipes.
  • To identify the most accurate automated method for reliable water level data collection.
  • To enhance the precision of sewage pipe condition assessments.

Main Methods:

  • DeepLabv3 was employed to extract segmentation masks from CCTV footage.
  • An Extra Trees regressor model was utilized, taking segmentation masks as input.
  • Six distinct automated water level estimation techniques were evaluated.

Main Results:

  • The Extra Trees regressor, using DeepLabv3 segmentation masks, demonstrated superior accuracy.
  • Evaluation metrics included Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and max error.
  • Automated methods significantly reduced the unreliability associated with manual water level recordings.

Conclusions:

  • Automated water level estimation in sewage pipes is feasible and more reliable than manual methods.
  • The proposed DeepLabv3 and Extra Trees regressor approach offers a robust solution for accurate data collection.
  • Improved data accuracy supports better decision-making in sewage pipe maintenance and structural integrity assessments.