An Enhanced Generative Adversarial Network Prediction Model Based on LSTM and Attention for Corrosion Rate in Pipelines

To address the pervasive issue of internal pipeline corrosion in the oil and gas industry, this paper proposes a hybrid intelligent model for predicting corrosion rates. This model integrates an improved Generative Adversarial Network with Grey Wolf Optimization and Support Vector Regression (LAGAN-GWO-SVR). In this model, the traditional Generative Adversarial Network is combined with the Long Short-Term Memory network and the Multi-head Attention mechanism. The Long Short-Term Memory is used to capture and analyze the sequential features in internal pipeline corrosion data, effectively uncovering latent relationships within the sequences. Meanwhile, the Multi-head Attention mechanism focuses on key features, further enhancing the model’s ability to concentrate on critical information. In addition, to more accurately predict the corrosion rate of internal pipeline in complex environments, this paper utilizes Grey Wolf Optimization to optimize the hyper-parameters in Support Vector Regression. Three sets of experiments were conducted, including different data augmentation algorithms, various improvement strategies, and comparisons with other benchmark models. The experimental results show that the model offers significant advantages in predicting internal pipeline corrosion rates. The LAGAN-GWO-SVR in this paper achieves a Root Mean Square Error of 0.013 and a coefficient of determination of 0.982.