首页|基于IPSO-EGA-LSTM模型的洞庭湖水位预测方案研究

基于IPSO-EGA-LSTM模型的洞庭湖水位预测方案研究

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准确的水位预测能够为湖区防洪减灾及水资源管理提供科学依据。通过引入IPSO改进粒子群算法及EGA精英遗传算法，优化LSTM长短期记忆神经网络结构，应用改进的IPSO-EGA-LSTM模型开展洞庭湖区各水位站1d预见期下的水位预测，与LSTM、GRU和BP神经网络模型进行精度对比，并评估该模型在更长预见期下(3d、5d、7d)的预测精度;进一步设置3种模型输入条件，提出相应水位预测方案(直接预测、同步预测、滚动预测)，探究各预报方案在不同预见期下的水位预测精度。结果表明，IPSO-EGA-LSTM模型对洞庭湖水位的预测效果优于传统神经网络模型，能够有效捕捉到不同预见期下洞庭湖水位变化趋势，1d预见期纳什效率系数(NSE)大于0。998，长预见期下NSE大于0。9;不同输入条件下的3种预报方案对洞庭湖水位均有较好预测效果，其中，同步预测方案在长预见期条件下比直接预测和滚动预测表现出更好的性能。
Water Level Prediction Schemes of Dongting Lake Based on IPSO-EGA-LSTM Model
Accurate water level prediction can provide scientific basis for flood control and disaster reduction and water resource management in lake area.In this paper,the IPSO improved particle swarm optimization algorithm and EGA elite genetic algorithm were introduced to optimize the LSTM long short term memory neural network structure.The improved IPSO-EGA-LSTM model was used to predict the water level of the gauging stations in Dongting Lake area under the Id forecast period.The accuracy of the model was compared with the LSTM,GRU and BP neural network models.The prediction accuracy of the model was evaluated under the longer forecast period(3d,5d and 7d).Three kinds of model input conditions were further set,corresponding water level prediction schemes were put forward,and the prediction accuracy of each forecasting scheme(direct forecasting,synchronous forecasting,rolling forecasting)under different forecast periods was explored.The results showed that the IPSO-EGA-LSTM model was better than the traditional neural network model in predicting the water level of Dongting Lake.It effectively captured the variation characteristics of water level of Dongting Lake in different forecast periods.The Nash efficiency coefficient(NSE)in 1d forecast period was greater than 0.998,and the NSE was still greater than 0.9 in long forecast period.The three prediction schemes under different input conditions demonstrated better prediction effects,among which the synchronous prediction scheme had better performance than the direct prediction and rolling prediction under the long prediction periods.

deep learningLSTMoptimization algorithmDongting Lake areawater level predictionforecast lead time

隆院男、潘鹤鸣、盛东、黄春福、宋昕熠、刘易庄

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长沙理工大学水利与环境工程学院,湖南长沙 410114

洞庭湖水环境治理与生态修复湖南省重点实验室,湖南长沙 410114

湖南省水利水电科学研究院,湖南长沙 410000

深度学习 LSTM 优化算法 洞庭湖 水位预测 预见期

国家自然科学基金面上项目湖南省研究生科研创新项目湖南省自然科学基金青年基金项目湖南省水利科技项目湖南省学位与研究生教育改革研究项目

52079010CX202209102021JJ40589XSKJ2019081-092022JGZD050

2024

10.11870/cjlyzyyhj202406011

长江流域资源与环境
中国科学院资源环境科学与技术局 中国科学院武汉文献情报中心

长江流域资源与环境

CSTPCDCSSCICHSSCD北大核心
影响因子：1.35
ISSN：1004-8227
年,卷(期)：2024.33(6)
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