GIS data standardization and algorithm optimization for intelligent drainage network monitoring

This study aims to solve the complexity and inconsistency problems of multi-source GIS data in the intelligent drainage network monitoring system, and proposes a set of data standardization and algorithm optimization solutions to improve the real-time monitoring accuracy and data quality of the system. By constructing a standardized model, the drainage network data is formatted, converted into units, and outliers are processed, achieving data integrity and consistency, laying the foundation for subsequent monitoring and analysis. In terms of algorithm optimization, the sliding mean filter, Kalman filter, and ARIMA model are combined to optimize the data cleaning, noise reduction, and prediction algorithms, significantly enhancing the system's processing capabilities for dynamic monitoring data and improving the accuracy and stability of predictions. The experimental results show that the synergy of data standardization and optimization algorithms can significantly improve the performance and efficiency of the intelligent drainage monitoring system, and provide strong support for drainage network anomaly detection and real-time decision-making.