Name: | Description: | Size: | Format: | |
---|---|---|---|---|
11.17 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Traditional methods for water-level measurement usually employ permanent structures,
such as a scale built into the water system, which is costly and laborious and can wash away with
water. This research proposes a low-cost, automatic water-level estimator that can appraise the level
without disturbing water flow or affecting the environment. The estimator was developed for urban
areas of a volcanic island water channel, using machine learning to evaluate images captured by a
low-cost remote monitoring system. For this purpose, images from over one year were collected. For
better performance, captured images were processed by converting them to a proposed color space,
named HLE, composed of hue, lightness, and edge. Multiple residual neural network architectures
were examined. The best-performing model was ResNeXt, which achieved a mean absolute error of
1.14 cm using squeeze and excitation and data augmentation. An explainability analysis was carried
out for transparency and a visual explanation. In addition, models were developed to predict water
levels. Three models successfully forecasted the subsequent water levels for 10, 60, and 120 min, with
mean absolute errors of 1.76 cm, 2.09 cm, and 2.34 cm, respectively. The models could follow slow
and fast transitions, leading to a potential flooding risk-assessment mechanism.
Description
Keywords
Water-level measurement Image processing Deep learning Qater stream channel Volcanic islands . Faculdade de Ciências Exatas e da Engenharia
Citation
Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F.; Azevedo, J.A.; Ravelo-García, A.G.; Navarro-Mesa, J.L. Noncontact Automatic Water Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learning. Electronics 2024, 13, 1145. https:// doi.org/10.3390/electronics13061145
Publisher
MDPI