目的 构建陆地长输管道外腐蚀速率的预测模型，提升管道外腐蚀速率预测的精度，对长输管道外腐蚀状态进行准确把控。方法 深入解析了萤火虫算法(FA)的工作原理，针对FA易出现陷入局部最优或因控制参数设置不合适而导致函数无法收敛等问题，提出了FA的改进方案:采用Logistics混沌映射的方法初始化萤火虫的位置，提升萤火虫种群的所养性;引入一种新的惯性权重计算方法来改进萤火虫位置移动公式，提升FA全局寻优能力。利用改进的萤火虫算法(IFA)对误差反向传播神经网络(BPNN)初始权值和阈值进行优化，建立基于IFA-BPNN的长输管道外腐蚀速率预测模型。以111组长输管道外腐蚀检测数据为例，在MATLAB中进行模拟仿真计算，使用粒子群算法优化的BPNN(PSO-BPNN)、遗传算法优化的BPNN(GA-BPNN)以及未进行优化的BPNN作为对比模型进行对比分析。结果 使用IFA优化BPNN，大幅提升了BPNN模型的预测精度。使用IFA-BPNN模型预测12组管道腐蚀速率，平均相对误差仅为5.94%，预测结果的R²为0.995 95，均优于BPNN、PSO-BPNN以及GA-BPNN模型的预测结果。结论 IFA-BPNN作为预测管道腐蚀速率工具具有较好的预测精度和鲁棒性。

In order to establish a machine learning model for predicting the external corrosion rate of long land transport pipelines, improve the prediction accuracy of the external corrosion rate of the pipeline, and accurately grasp the external corrosion status of the long-distance pipeline, this paper analyzes the working principle of FA, to solve the problems of FA, such as local optimization or function convergence failure due to initial parameter setting, and an improved FA algorithm is proposed:This paper uses the method of Logistics chaotic mapping to initialize the position of the firefly, and improve the cultivability of the firefly population; this paper introduces a new inertia weight calculation method to improve the formula of the firefly position movement and enhance the FA global optimization ability. The improved FA (IFA) was used to optimize the initial weights and thresholds of BPNN, and a long-distance pipeline external corrosion rate prediction model based on IFA-BPNN was established. Taking 111 sets of long-distance pipeline external corrosion detection data as an example, the simulation calculation is carried out in MATLAB, and PSO-BPNN, GA-BPNN and unoptimized BPNN are used as comparative models for comparative analysis. The IFA model is used to initialize the BPNN model, which greatly improves the prediction accuracy of the BPNN model. The IFA-BPNN model was used to predict and analyze the external corrosion rates of 12 groups of pipelines, the average relative error was only 5.94%, and the R2 of the prediction results was 0.995 95. The prediction results of IFA-BPNN model are superior to those of BPNN model, PSO-BPNN model and GA-BPNN model in all aspects. IFA-BPNN has good accuracy and robustness as a tool to predict pipeline corrosion rate.