Water conservancy big data research methods are as follows:

1, data collection and pre-processing: water conservancy big data collection includes a variety of means such as sensors, remote sensing technology, etc., and data pre-processing refers to the cleaning, denoising, normalization and other operations of the data in order to improve the quality and reliability of the data.

2, data mining and analysis: water conservancy big data mining and analysis mainly includes data classification, clustering, correlation analysis, time series analysis and other aspects. These methods convert large amounts of data into more meaningful information and knowledge, thus helping decision makers better understand and analyze water issues.

3. Machine Learning and Artificial Intelligence: Machine Learning and Artificial Intelligence techniques allow computers to automatically learn and adapt to new data patterns, thereby inferring potential patterns and trends and extracting core information from the data. These technologies have a wide range of applications in water resource management, flood warning, and water quality monitoring.

4, modeling and simulation: water conservancy big data modeling and simulation can help simulate and predict the changes and evolution of water resources. By modeling and simulating different data scenarios, the effects and risks of water resources development and utilization strategies can be better assessed.

5, visualization and interactive analysis: visualization and interactive analysis techniques can convert data into an intuitive, easy-to-understand form, to help users better understand water issues. These technologies include data visualization, interactive analysis, virtual reality and other aspects that allow users to explore and analyze data in multiple dimensions.