Research on intelligent slope stability assessment algorithm by integrating multi-source data

Slope stability assessment is a key issue in the prevention and control of geologic hazards, and traditional methods often rely on limited single data sources and empirical models, which are difficult to accurately reflect the complex and changing geological environment. In order to improve the accuracy and timeliness of the assessment, this paper proposes an intelligent slope stability assessment algorithm that integrates multi-source data. The method integrates remote sensing images, geological survey data and real-time monitoring data, constructs a unified feature space through multi-level data fusion technology, and introduces multiple machine learning models to realize intelligent prediction of slope stability state. The research focuses on the preprocessing and fusion strategy of heterogeneous data from multiple sources, the identification and extraction of key influencing factors, and the design of evaluation models applicable to different geological scenarios. The proposed algorithm is verified to be superior to traditional methods in terms of assessment accuracy, robustness and generalization ability through empirical analysis of typical case areas. The results show that the combination of multi-source data fusion and intelligent algorithms provides a more scientific and efficient technical path for slope safety prediction, which has a broad engineering application prospect.