基于ISSA-BiLSTM的供水泵站流量预测OA

The optimal scheduling and energy-efficient operation of urban water supply pumping stations are critically dependent on the precise prediction of water flow.Accurate forecasting is a cornerstone of intelligent water management,allowing proactive control of pump operations to stabilize pressure within the distribution network,reduce energy consumption,and improve the overall safety and reliability of the water supply system.However,flow data from these stations typically show pronounced non-linearity,complex periodicities,and random fluctuations,which are influenced by a variety of interconnected factors such as consumer behavior,meteorological conditions,and network pressure.Modeling long-term dependencies has proven to be a major challenge for traditional forecasting techniques,such as statistical models and traditional machine learning algorithms.Furthermore,the performance of advanced deep learning models is frequently limited by the challenge of hyperparameter optimization,which frequently relies on empirical adjustments and is prone to suboptimal local results. To address the stated challenges,a novel hybrid deep learning framework was designed for water supply flow prediction,which integrated an improved sparrow search algorithm(ISSA)with a Bi-directional long short-term memory(BiLSTM)network incorporating an attention mechanism.First,a logistic chaotic map was used to initialize the sparrow population.This was one of the major changes made to the traditional sparrow search algorithm(SSA)to improve its optimization capability.By ensuring a more consistent and varied initial search agent distribution,this technique successfully keeps the algorithm from entering local optima in its early phases.Secondly,an adaptive Levy flight strategy,coupled with dynamic cosine weights,was implemented to balance the algorithm's global exploration and local exploitation capabilities throughout the iterative process.This enhanced ISSA was then utilized to systematically and automatically optimize the critical hyperparameters of the BiLSTM network,including the number of hidden units,learning rate,and regularization coefficients. A comprehensive dataset gathered over 19 months from a large-scale water supply pumping station was used to train and validate the model.Rigorous data preprocessing was performed,involving anomaly detection using an Isolation Forest algorithm and feature selection based on Pearson correlation analysis,which identified thirteen key operational features such as flow rates,pressure levels,and valve opening percentages. The suggested ISSA-BiLSTM model performed better,according to the empirical evaluation.The model achieved a root mean square error(ERMS)of 28.75 m3/h,a mean absolute error(EMA)of 14.03 m3/h,a mean absolute percentage error(EMAP)of 0.12%,and a coefficient of determination(R2)of 0.89 on the test dataset.A comprehensive comparative analysis was performed against a wide range of baselines,including advanced deep learning variants(Transformer,TCN),ensemble learning models(XGBoost,LightGBM),and other metaheuristic-optimized models(GA-BiLSTM,GWO-BiLSTM).The results revealed that the proposed model significantly outperformed these competitors,reducing the prediction error by approximately 62.7%compared to the standard LSTM model and showing statistically significant improvements over other optimized variants.The results confirm that the suggested framework offers a very reliable and accurate way to predict flow in water supply pumping stations,providing a substantial technical basis for the development of intelligent operational scheduling and fine-grained management in contemporary urban water supply systems.