Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Identification and Quantification of Hidden Dangers in Small Scale Reservoir Engineering Based on Deep Learning for Flood control, water supply and irrigation

Zhiwei Zhou
,
Shibiao Fang
* ORCID logo ,
Weihua Fang
,
Yaozong Xu
,
Bin Zhu
,
Lei Li
,
Haixiang Ji
,
Wenrong Tu
*
Version 1 : Received: 25 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (17:03:58 CEST)

A peer-reviewed article of this Preprint also exists.

Zhou, Z.; Fang, S.; Fang, W.; Xu, Y.; Zhu, B.; Li, L.; Ji, H.; Tu, W. The Identification and Quantification of Hidden Hazards in Small Scale Reservoir Engineering Based on Deep Learning: Intelligent Perception for Safety of Small Reservoir Projects in Jiangxi Province. Water 2024, 16, 2880. Zhou, Z.; Fang, S.; Fang, W.; Xu, Y.; Zhu, B.; Li, L.; Ji, H.; Tu, W. The Identification and Quantification of Hidden Hazards in Small Scale Reservoir Engineering Based on Deep Learning: Intelligent Perception for Safety of Small Reservoir Projects in Jiangxi Province. Water 2024, 16, 2880.

Abstract

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding-decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by UAVs. The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) was obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers)proposed in this paper outperforms other methods. The main evaluation indicator mIoU of HHSN-25 is higher than other methods, reaching 87.00%, and Unet is 85.50%, Unet++is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

Keywords

hidden dangers; small reservoir engineering; deep learning; semantic segmentation; water conservancy project

Subject

Engineering, Safety, Risk, Reliability and Quality

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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