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Precipitation Titration: Endpoint Detection Methods

Precipitation Titration: Endpoint Detection Methods

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Related Experiment Video

Updated: Jul 7, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Toward accurate and scalable rainfall estimation using surveillance camera data and a hybrid deep-learning framework.

Fiallos-Salguero Manuel¹, Soon-Thiam Khu¹, Jingyu Guan²

¹School of Environmental Science & Engineering, Tianjin University, Tianjin, 300350, China.

Environmental Science and Ecotechnology

|May 20, 2025

Summary

This study introduces a new framework using surveillance cameras for accurate rainfall estimation, improving urban hydrological modeling and stormwater management. The novel approach enhances data coverage and accuracy, offering a practical solution for real-time monitoring.

Keywords:

Computer vision algorithms Rainfall estimation Regression Spatial-temporal resolution Surveillance video

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Area of Science:

Hydrology and Remote Sensing
Computer Vision for Environmental Monitoring

Background:

High-quality rainfall data is crucial for urban hydrology and stormwater management.
Traditional methods have limitations in spatial coverage, temporal resolution, and accessibility in urban areas.

Purpose of the Study:

To develop a novel rainfall estimation framework using surveillance cameras.
To enhance accuracy and spatiotemporal data coverage for rainfall measurement.

Main Methods:

A hybrid approach combining image-quality signature for rain streak detection and optimal region selection.
Integration of depthwise separable convolution (DSC) and gated recurrent units (GRU) for rainfall intensity estimation.

Main Results:

The DSC-GRU model achieved high predictive performance (R² values of 0.89-0.93) against rain gauge data.
The framework demonstrated robust performance in both daytime and nighttime conditions across diverse environments.
Outperformed existing video-based rainfall estimation methods.

Conclusions:

The novel framework provides a practical, low-cost solution for real-time urban rainfall monitoring.
Significantly reduces estimation errors and expands measurement coverage for hydrological applications.
Represents a substantial advancement in rainfall estimation technology.