• (1)2008-2021年连续14年获得中国科技核心期刊
  • (2)2021年被中国科学院文献情报中心中国科学引文数据库CSCD(核心库)收录(2021-2022)
  • (3)热烈庆祝《水资源与水工程学报》入编北京大学图书馆《中文核心期刊要目总览》2020年版
王肖鑫, 岑威钧, 李昭辉, 吴光华.基于人工电场算法优化的大型灌区径流预测模型研究水资源与水工程学报[J].,2022,33(4):79-84
基于人工电场算法优化的大型灌区径流预测模型研究
Runoff prediction model for large irrigation areas based on artificial electric field algorithm optimization
  
DOI:10.11705/j.issn.1672-643X.2022.04.11
中文关键词:  径流预测  人工电场算法  AEFA-LSTM模型  参数优化  灌区
英文关键词:runoff prediction  artificial electric field algorithm (AEFA)  AEFA-LSTM model  parameter optimization  irrigation area
基金项目:
作者单位
王肖鑫1, 岑威钧1, 李昭辉2, 吴光华2 (1.河海大学 水利水电学院 江苏 南京 210098 2.河南省赵口引黄灌区二期工程建设管理局 河南 周口 466623) 
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中文摘要:
      针对水文预报中径流预测数据序列具有非线性和非平稳性等特点,将一种新型智能优化算法——人工电场算法AEFA与LSTM神经网络结合进行参数优化,建立AEFA-LSTM预测模型,并以赵口大型灌区涡河玄武水文站实测年径流量作为样本数据进行网络优化训练和预测分析,同时与传统优化算法(遗传算法GA和粒子群算法PSO)建立的GA-LSTM和PSO-LSTM预测模型进行对比。结果表明:AEFA-LSTM模型预测值的平均相对误差相较于GA-LSTM模型和PSO-LSTM模型分别降低了7.59%和5.22%,且平均绝对误差MAE、均方误差MSE、均方根误差RMSE均为3种模型中最小,说明所建立的AEFA-LSTM模型可以更高精度地预测径流量,为水文预报提供一种新型高精度径流预测方法。
英文摘要:
      In view of the nonlinear and non-stationary characteristics of runoff prediction data series in the hydrological prediction, a new intelligent optimization algorithm, artificial electric field algorithm (AEFA), is combined with LSTM neural network to optimize the network parameters, then the AEFA-LSTM prediction model is established. The measured annual runoff of Xuanwu Hydrological Station of Wohe River in Zhaokou Large Irrigation Area was then used as the sample data for the network optimization training and prediction analysis, and the results were compared with those of the GA-LSTM model and PSO-LSTM model which were established using conventional optimization algorithms (GA and PSO). The comparison analysis shows that compared with GA-LSTM model and PSO-LSTM model, the average relative error of the predicted value calculated by the AEFA-LSTM model is reduced by 7.59% and 5.22% respectively and its average absolute error, mean square error and root mean square error are the smallest of the three models, indicating that the AEFA-LSTM model can predict the runoff more accurately, and provide a new high-precision runoff prediction method for the hydrological prediction.
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