基于AGA-BiLSTM的降水预测方法  

Precipitation prediction method based on AGA-BiLSTM

作　　者：李冰洁 于霞[2] 于丹丹[1] LI Bingjie;YU Xia;YU Dandan(The First Affiliated Hospital of Dalian Medical University,Dalian,Liaoning 116001,China;Shenyang University of Technology,Shenyang 110870,China)

机构地区：[1]大连医科大学附属第一医院,辽宁大连116011 [2]沈阳工业大学,沈阳110870

出　　处：《计算机应用文摘》2025年第1期115-117,共3页

摘　　要：准确的降水预测在许多应用领域被广泛认为是一项重要任务。然而,真实世界的降水数据通常具有高度非线性和复杂性,这使得传统预测技术难以获得精确结果。为了解决这些问题,文章提出了一种改进的AGA-BiLSTM模型,旨在克服BiLSTM在特征提取能力、长序列处理能力以及参数调优方面的局限性。该模型在BiLSTM网络的基础上进行创新,即引入注意力机制放大关键信息,模型能够有效提取降水数据中的主要特征,同时抑制对次要信息的过度关注,提高预测的准确性;引入遗传算法自动全局优化模型参数,从而节省人工调优的时间成本,同时避免模型陷入局部最优解的困境。实验结果表明,AGA-BiLSTM模型在降水预测任务中表现出色,能够有效处理复杂的降水数据特性,为降水预测问题提供了一个高效的解决方案。Accurate precipitation prediction is widely regarded as an important task in many application fields.However,real-world precipitation data often has high nonlinearity and complexity,which makes it difficult for traditional forecasting techniques to obtain accurate results.To address these issues,the article proposes an improved AGA-BiLSTM model aimed at overcoming the limitations of BiLSTM in feature extraction capability,long sequence processing capability,and parameter tuning.This model innovates on the basis of BiLSTM network by introducing attention mechanism to amplify key information.The model can effectively extract the main features of precipitation data while suppressing excessive attention to secondary information,improving the accuracy of prediction.Introducing genetic algorithm to automatically optimize model parameters globally,thereby saving time and cost of manual tuning,while avoiding the dilemma of the model getting stuck in local optimal solutions.The experimental results show that the AGA-BiLSTM model performs well in precipitation prediction tasks and can effectively handle complex precipitation data characteristics,providing an efficient solution for precipitation prediction problems.

关 键 词：BiLSTM 注意力机制 遗传算法 降水预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]