Research on the outlier value analysis algorithm of water conservancy engineering quality detection based on machine learning

Abstract: This study aims to improve the accuracy of the quality detection of water conservancy projects, and to identify and analyze the outliers in the detection data by introducing machine learning algorithms. A variety of technical techniques, including data preprocessing, feature extraction, supervised learning and unsupervised learning, were used to construct and optimize the outlier detection model. The experimental results show that the machine learning-based detection method significantly improves the accuracy and efficiency of outlier identification, especially when using deep learning models. The research conclusion points out that the machine learning algorithm has obvious advantages in processing complex and massive monitoring data, and can be widely used in the quality monitoring and management of water conservancy projects, providing a strong guarantee for the engineering safety.