A Predictive Model for the Operational Status of Water Conservancy Facilities Based on Big Data Analysis and Cloud Computing

The complexity and variability of environmental factors make it difficult to predict the operation state of water conservancy facilities in the long-term operation process, which directly affects the effective maintenance and management of facilities. Based on this, this article puts forward a prediction model of water conservancy facilities' operation state based on bidirectional long-term and short-term memory network (BiLSTM). Based on the integration of sensor monitoring data, meteorological information and historical operation data, this article uses the powerful computing power of big data technology and cloud computing platform to realize efficient prediction of water conservancy facilities' operation state. Firstly, a comprehensive sensor network is established to collect key operation parameters such as structural health status of water conservancy facilities in real time, and then these data are combined with historical operation data and meteorological information to form a multi-source data set, and then these data are preprocessed by data cleaning, denoising and feature extraction to ensure the quality and consistency of data input into the model. Then BiLSTM is used to process time series data. By deploying BiLSTM model on the cloud computing platform, the flexible computing resources of cloud services are used for model training and prediction to improve efficiency. In order to further improve the prediction accuracy, this study also combines with polymorphic Bayesian network to post-process the output of BiLSTM. Finally, the trained model is used to predict the operation state of water conservancy facilities, and the prediction results are evaluated and verified through actual operation data to realize early identification and early warning of potential risks. The experimental results show that BiLSTM model is significantly superior to the traditional recurrent neural network (RNN) model in prediction accuracy, with an average accuracy of 95.18%, while the average accuracy of RNN model is only 77.92%. BiLSTM model also shows higher efficiency in prediction time and CPU utilization. Finally, the BiLSTM-based water conservancy facilities operation state prediction model constructed in this study provides a new solution for intelligent management of water conservancy facilities.