In recent years, with the continuous progress of science and technology, water conservancy projects are playing an increasingly important role in social economy. However, the risk accidents caused by the lack of traditional engineering experience and human factors are also gradually increasing. As one of the national infrastructure construction projects, the importance of water conservancy is self-evident. Therefore, how to improve the quality, reliability and operation efficiency of water conservancy projects through computer technology is very necessary and has practical significance. This paper first introduces the commonly used mathematical statistics methods and the application status of machine learning algorithm, and then uses machine learning method to build probabilistic neural network technology to study the role of artificial neural network in risk prediction of water conservancy projects. Finally, the numerical simulation software is used to convert the risk factor relationship analysis results into actual data. The test results show that the impact between natural disasters and engineering geological conditions is the greatest. Therefore, in the risk prediction of water conservancy projects, special attention should be paid to the concurrent impact results of these two.

Hongrong Hou. Relationship between Risk Factors of Water Conservancy Project Based on Machine Learning Algorithm. Machine Learning Theory and Practice (2023), Vol. 4, Issue 1: 35-43. https://doi.org/10.38007/ML.2023.040105.

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