A Novel Optimal Layout Method for Rain Gauge Network Based on Mutual Information Entropy and Deep Learning Model.

Area of Science:

• Hydrology
• Water Resource Management
• Geospatial Analysis

Background:

• Rain gauge networks are crucial for water resource management and disaster mitigation.
• Network layout directly impacts precipitation data accuracy and representativeness.
• Optimizing rain gauge placement is essential for efficient data acquisition.

Purpose of the Study:

• To develop a novel optimal layout method for rain gauge networks.
• To integrate mutual information entropy with spatiotemporal deep learning for rainfall analysis.
• To enhance the accuracy and efficiency of hydrological monitoring.

Main Methods:

• Coupling mutual information entropy with a CNN-LSTM spatiotemporal deep learning model.
• Utilizing a GA-PSO hybrid optimization algorithm for network design.
• Analyzing daily rainfall data from ground-based gauges and remote sensing products.

Main Results:

• The proposed method significantly outperforms traditional algorithms in network structure rationality and spatial rainfall characterization.
• The optimized network effectively controls average relative error and improves total information entropy.
• The number of rain gauges can be reduced by 50% without compromising data quality.

Conclusions:

• The hybrid optimization algorithm driven by spatiotemporal deep learning and mutual information entropy offers a superior approach for rain gauge network design.
• This method provides a theoretical basis and technical support for optimizing basin rain gauge networks and water resource management.
• The optimized network effectively captures rainfall spatiotemporal characteristics, enabling efficient monitoring and management.