Hydrogeological Environmental Data Mining and Intelligent Algorithm Application: Water Resources Management Under the Background of Big Data

On the one hand, hydrogeological environmental data are massive, complex and widely sourced, and traditional data analysis methods are difficult to efficiently extract valuable information from them, resulting in inaccurate understanding of water resource conditions and changing trends. On the other hand, water resource management decisions lack accurate data support, making it difficult to achieve scientific and efficient allocation and protection. This paper adopts a method that combines the FP-Growth algorithm with the Feedforward Neural Network (FNN) to improve the scientificity and accuracy of water resource management. The paper collects various hydrogeological environmental data including groundwater level, water quality parameters, surface runoff data, soil moisture content, etc., and integrates data resources from different monitoring sites and different time scales. Then, the FP-Growth algorithm is used to analyze the collected data and explore the correlation between different hydrogeological elements. Then, FNN is introduced, and the hydrogeological environmental data after preprocessing and feature extraction is used as input to learn the complex nonlinear relationship in the data, thereby realizing accurate prediction of the dynamic changes of water resources. At the same time, the K-means clustering algorithm is used to cluster the hydrogeological environment of different regions according to data characteristics, identify regions with similar water resource characteristics, and provide a basis for water resource zoning management and targeted protection measures. Based on the management strategy proposed by the above method, the average water quality compliance rate reaches 85.8 %, the wetland area retention rate increases, and the proportion of available water resources reaches 79.5 %. These results prove the superiority of this method in improving water resource management.