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Multi-Source Error Compensation for Weighing Rain Gauge Based on Adaptive GOOSE-BP Network
1Intelligent Equipment Laboratory, College of Engineering, Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing 100083, China.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
This study developed an adaptive error compensation framework for weighing rain gauges, significantly improving measurement accuracy by addressing environmental disturbances. The new ADGOOSE-BP model offers a robust solution for precise hydrological monitoring.
Area of Science:
- Hydrology
- Environmental Science
- Measurement Science
Background:
- Weighing rain gauges suffer inaccuracies due to environmental factors like vibration and temperature drift.
- Existing compensation methods struggle with complex, nonlinear error patterns.
Purpose of the Study:
- To develop a robust error modeling and compensation framework for weighing rain gauges.
- To enhance measurement accuracy under complex environmental conditions, including vibration and temperature variations.
Main Methods:
- Constructed a nonlinear error model incorporating linear and nonlinear temperature terms.
- Utilized a BP neural network optimized by genetic algorithm (GA), particle swarm optimization (PSO), and GOOSE algorithm.
- Proposed an improved adaptive GOOSE algorithm (ADGOOSE) for enhanced BP network training.
Main Results:
- The ADGOOSE-BP model achieved a Root Mean Square Error (RMSE) of 0.0494 and R-squared (R2) of 0.9835.
- Demonstrated superior performance compared to traditional filtering and other optimization techniques.
- Validated effectiveness across various rainfall intensities and temperatures.
Conclusions:
- The proposed method effectively models and compensates for environmentally induced errors in weighing rain gauges.
- The ADGOOSE-BP framework shows strong potential for high-precision, adaptive compensation.
- Provides a foundation for future field-deployable hydrological monitoring systems.
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