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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 Monitoring
- Sensor Technology
Background:
- Weighing rain gauges suffer inaccuracies due to environmental factors like vibration and temperature drift.
- Existing error compensation methods lack adaptability to complex, real-world conditions.
Purpose of the Study:
- To develop a robust error modeling and compensation framework for weighing rain gauges.
- To enhance measurement accuracy by addressing environmental disturbances such as vibration, temperature drift, and creep.
Main Methods:
- Constructed a nonlinear error model incorporating linear and nonlinear temperature terms.
- Employed a BP neural network optimized by genetic algorithm (GA), particle swarm optimization (PSO), and GOOSE algorithm.
- Proposed an improved adaptive GOOSE (ADGOOSE) algorithm with dynamic control coefficients and restart strategies for BP network optimization.
Main Results:
- The ADGOOSE-BP model demonstrated superior performance over traditional methods.
- Achieved a root mean square error (RMSE) of 0.0494 and an R-squared (R2) value of 0.9835 under various conditions.
- Validated effectiveness across different rainfall intensities and temperatures.
Conclusions:
- The proposed method effectively models and compensates for environmentally induced errors in weighing rain gauges.
- ADGOOSE-BP offers a high-precision, adaptive compensation framework.
- Provides a foundation for field-deployable, high-accuracy hydrological monitoring systems.
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