A method for identifying the water storage state of a silt dam in the loess plateau region

A deep learning method was used to construct a water storage status identification model for silt-retaining dams, which solved the problem of time-consuming and labor-intensive traditional remote sensing image interpretation methods. This model enables efficient monitoring of the water storage status of silt-retaining dams and is applicable to the water conservancy project management of silt-retaining dams and earth-rock dams in the Loess Plateau region.

CN117372889BActive Publication Date: 2026-06-16NORTHWEST ENGINEERING CORPORATION LIMITED +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NORTHWEST ENGINEERING CORPORATION LIMITED
Filing Date
2023-10-27
Publication Date
2026-06-16

AI Technical Summary

⚠Technical Problem

Traditional remote sensing image interpretation methods are time-consuming and labor-intensive, making it impossible to achieve long-term, large-scale, and high-frequency monitoring and analysis of the water storage status of silt-retaining dams in the Loess Plateau region. There is a lack of efficient monitoring and analysis methods.

⚗Method used

A deep learning target recognition model for the water storage status of silt-retention dams was constructed using high spatial resolution optical remote sensing images and convolutional neural networks. The model was trained through data augmentation and transfer learning to achieve rapid identification of the water storage status of silt-retention dams.

🎯Benefits of technology

It improves the efficiency of monitoring the water storage status of silt-retaining dams, achieves high-precision and high-frequency monitoring, overcomes the reliance on human subjective experience in traditional methods, and is applicable to the management of water conservancy projects such as large-scale silt-retaining dams and earth-rock dams.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a kind of for loess plateau region silt dam impoundment state identification method, belong to water and soil identification technical field.The method includes: obtaining high spatial resolution optical remote sensing image and processing, making silt dam target identification dataset;According to silt dam target identification dataset, construct and train silt dam impoundment state deep learning target identification model;The high spatial resolution optical remote sensing image to be measured is input to silt dam impoundment state deep learning target identification model after processing, after silt dam impoundment state deep learning target identification model identification, output identification result, and identification result includes silt dam visual image and silt dam data.The application can be quickly identified to the silt dam target with wide spatial distribution range, improve the work efficiency of silt dam target identification and impoundment state analysis using remote sensing, solve the problem that silt dam lacks effective supervision method for a long time.
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