基于融合注意力机制LSTM网络的地下水位自适应鲁棒预测OACSTPCD

Groundwater level is an important factor affecting groundwater infiltration of sewage pipe network in dry weather.Accurate prediction of groundwater level can effectively improve the accuracy of groundwater infiltration estimation in dry weather,and assist in optimizing pipe net-work disease control and maintenance strategies.Aiming at the problems of low accuracy,low sensitivity,and weak generalization ability in the current urban complex hydrological prediction,a new robust adaptive water level prediction algorithm was proposed in this paper.First,a prior processing was carried out on the hydrological data,which solved the problems of large time span,high noise,missing and abnormal,and non-sta-tionary data.Secondly,in view of the influence difference of input features on predictive indicators,a new spatial variable attention mechanism was proposed in model training stage,which can quickly identify key variables associated with water levels and assign different influence weights to input features.Furthermore,in view of the influence difference of various sequence lengths on the prediction effect,an adaptive temporal atten-tion mechanism was also designed to adaptively find out the hidden state of the encoder related to the predictors of different sequence lengths,so as to capture time dependencies.On this basis,with the context vector as the input,an LSTM hydrological prediction algorithm integrating atten-tion mechanism was proposed.Finally,the effectiveness of the proposed algorithm was verified by the hydrological data of Petrignano,Italy.The prediction performance was compared with GRU,Elman,LSTM,VA-LSTM and S-LSTM methods.The results showed that the proposed STA-LSTM network based on the fusion attention mechanism has a better prediction effect than other algorithms when faced with complex,large-scale,and noisy data,indicating the strong adaptability and robustness of the algorithm.The research results of the paper provide a reference for the reasonable adjustment and timely control of municipal drainage strategies.