Corrosion Rate Prediction of Buried Oil and Gas Pipelines: A New Deep Learning Method Based on RF and IBWO-Optimized BiLSTM–GRU Combined Model

The corrosion of oil and gas pipelines represents a significant factor influencing the safety of these pipelines. The extant research on intelligent algorithms for assessing corrosion rates in pipelines has primarily focused on static evaluation methods, which are inadequate for providing a comprehensive dynamic evaluation of the complex phenomenon of corrosion in buried oil and gas pipelines. This paper proposes a novel approach to predicting the corrosion rate of buried oil and gas pipelines. The method is based on the combination of an improved Beluga Optimization algorithm (IBWO) and Random Forest (RF) optimization with BiLSTM and gated cycle unit (GRU), which are used to classify corrosion rates as high or low. Initially, a feature screening of corrosion factors was conducted via RF, whereby variables exhibiting a strong correlation were extracted. Subsequently, IBWO was employed to optimize the feature selection process, with the objective of identifying the optimal feature subset to enhance the model’s performance. Ultimately, the BiLSTM method was employed for the purpose of predicting the occurrence of low corrosion. A GRU method was employed for prediction in the context of high corrosion conditions. The RF–IBWO-BiLSTM–GRU model constructed in this paper demonstrates high prediction accuracy for both high and low corrosion rates. The verification of 100 groups of experimental data yielded the following results: the mean square error of this model is 0.0498 and the R2 is 0.9876, which is significantly superior to that of other prediction models. The combined model, which incorporates an intelligent algorithm, is an effective means of enhancing the precision of buried pipeline corrosion rate prediction. Furthermore, it offers a novel approach and insight that can inform subsequent research on the prediction of corrosion rates in buried oil and gas pipelines.