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Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm-back propagation model.

Zhuang Xiong^1,2, Jun Ma¹, Bohang Chen¹

¹The College of Computer, Qinghai Normal University, Xining, China.

Frontiers in Big Data

|January 28, 2025

Summary

This summary is machine-generated.

This study introduces a two-layer genetic algorithm-backpropagation (GA-BP) model to improve rainfall data accuracy by identifying and fusing sensor data. The novel approach enhances fault recognition and data fusion, leading to more robust rainfall measurements.

Keywords:

BP neural network genetic algorithm–optimized back propagation network legacy algorithm multi-sensor fault recognition multi-source data fusion

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Area of Science:

Environmental Science
Computer Science
Data Science

Background:

Traditional rainfall data collection methods using rain buckets and meteorological data often overlook sensor fault impacts on accuracy.
Existing models may not effectively handle multi-source data identification and fusion in the presence of sensor errors.

Purpose of the Study:

To propose and evaluate a novel two-layer genetic algorithm-backpropagation (GA-BP) model for accurate rainfall data collection.
To enhance the identification and fusion of multi-source rainfall data, specifically addressing sensor faults.
To improve the robustness and generalization of rainfall measurement systems.

Main Methods:

A two-layer GA-BP model was developed, utilizing rainfall data from a sensor array.
The first GA-BP layer optimizes weights and thresholds for sensor fault recognition.
The second GA-BP layer performs data fusion based on identified fault data.

Main Results:

The two-layer GA-BP model reduced data fusion runtime by 2.37 seconds compared to a single-layer BP model.
Recognition accuracies for signal loss, high-value bias, and low-value bias improved by 26.09%, 18.18%, and 7.15%, respectively.
The mean squared error was reduced by 3.49 mm, and the fusion output waveform showed less fluctuation.

Conclusions:

The proposed two-layer GA-BP model significantly improves rainfall data accuracy and reliability.
The model demonstrates enhanced robustness and generalization capabilities in handling sensor faults.
This approach offers a more effective solution for multi-source rainfall data identification and fusion.