泵站机组运行受多种因素影响，导致泵站运行理论效率与实际效率误差较大。针对泵站机组运行效率精准模拟难题，运用基于高价多项式回归、回归树、多元线性回归、向量机回归、高斯过程回归、神经网络的10个回归算法，建立泵站机组效率模拟模型并开展对比分析，优选出有效的泵站运行效率模拟建模方法。讨论分析采用 “上下游水位+流量”代替传统“扬程+流量”开展泵站运行模拟的效果。以南水北调东线邳州站和遂宁二站共8台机组的历史数据开展实例分析，相关实验结果表明：在所有方法中，高斯过程回归（Gaussian process regression, GPR）模型在均方根误差（ERMS）、平均绝对误差（EMA）、均方误差（EMS）、决定系数（R2）和最大个体误差（EMI）指标上综合表现最佳，R2逼近0.95；使用站上、站下水位代替传统的扬程对模型进行训练，所有模型的综合评价指标整体有所改善。综合来看，使用GPR模型并使用上游、下游水位代替扬程进行模拟效率表现最好，以邳州站4号机为例，可将模拟效率的 EMA 和 EMI分别从16.49%和20.40%减少至0.41%和2.30%，研究成果具有一定实际意义，可为我国调水工程泵站经济运行提供有力支撑。

With China's extensive water transfers projects underway, the focus has shifted towards optimizing their operation, highlighting the significance of pumping station efficiency studies. The efficiency characteristic curve, a fundamental feature, plays a crucial role in optimizing station operation by utilizing measured head-flow data. However, long-term operation introduces efficiency errors, stemming from design inaccuracies, mechanical losses, fluid friction, operational errors, and improper maintenance. This is evident in cases like pumping Unit 4 at the Pizhou station, where substantial disparities between actual and theoretical efficiency exist, necessitating precise efficiency simulation models to align optimization schemes with the actual optimal state. Recent endeavors have integrated machine learning algorithms like polynomial regression, Gaussian process regression, and neural networks into hydraulic forecasting and simulation, offering promising avenues for pumping station efficiency simulation. Therefore, employing artificial intelligence techniques to investigate pumping station efficiency simulation was proposed, focusing on a representative station of the Eastern Route of the South-to-North Water Transfers Project.The efficiency simulation of pumping units in water management systems is a critical task, demanding meticulous preprocessing of operational data and the selection of appropriate modeling techniques. Initially, data preprocessing involves aligning time-stamped measurements, clustering data into windows, and handling anomalies to ensure data quality. Various influencing factors, such as flow rates, water levels, and blade rotating angles, are scrutinized to optimize efficiency modeling. Traditional methods, like Polynomial Regression and Multivariate Linear Regression, are contrasted with advanced techniques including decision regression trees, support vector regression, Gaussian process regression, and neural networks. Each method offers unique advantages, such as the interpretability of decision trees and the flexibility of neural networks. Training these models involves careful parameter selection and validation using established metrics like root mean squared error and determination coefficient. Python and MATLAB are prominent tools used for implementation, offering libraries and functions tailored for regression tasks. The average indicators of the eight pumping units indicate that GPR (Gaussian process regression) models with three different kernel functions (RQ, SE, E) exhibit the best overall performance in simulating the efficiency of the four units at the Pizhou station and the four units at the Suining station. The indicator shows the three GPR models are around 0.34 to 0.36, while ANN, DNN, and MLR are slightly above 0.5, with other models showing poorer performance. In terms of the R2 indicator, except for DRT and SVM models, which are approximately between 0.7 and 0.8, all other models score above 0.9. Regarding the EMI indicator, traditional polynomials (2nd PR and 3rd PR) perform the worst, while other models are within approximately 5, with the three GPR models ranging from 3.2 to 3.5, showing better performance. Considering the five metrics ERMS,EMA,R2,EMS and EMI, the GPR models demonstrate the best overall performance among various traditional and machine learning methods in the comprehensive testing. Comparing efficiency simulation methods, incorporating station upstream and downstream water levels alongside traditional head as features yielded superior results, notably enhancing model performance in various metrics. This approach, particularly evident in GPR models, addresses non-linear relationships and potential error sources in head calculations. Utilizing station water levels directly improves model accuracy, offering a more intuitive analysis of influencing factors.In conclusion, after analyzing ten regression models for pump efficiency simulation, the GPR model emerged as the most effective, outperforming traditional polynomial methods. Evaluation metrics showed significantly superior performance of GPR over other models, evidenced by reduced errors across various indicators when applied to training datasets of eight pump units at two stations. Substituting station water levels for traditional head as input features yielded notable improvements in model accuracy, particularly evident in GPR models. For instance, using GPR for efficiency simulation at one pump unit resulted in average and maximum absolute errors within 0.50% and 3.20% respectively, while employing water levels instead of head further reduced these errors to 0.41% and 2.30%. This enhancement signifies a substantial improvement over current methods, offering precise efficiency simulation crucial for optimizing pump station operations.