现代电子技术2024，Vol.47Issue(2)：176-182,7.DOI:10.16652/j.issn.1004-373x.2024.02.032
基于HHO优化的时空水质预测模型
Spatio-temporal water quality prediction model based on HHO optimization
摘要
Abstract
The current situation of water resource is not optimistic,so improving the accuracy of water quality prediction models is important for water quality monitoring.In order to capture the nonlinear trend of time series data of water quality index,water quality index is mostly predicted based on neural network model.However,the existing models ignore the flow direction of the river and do not consider the influence of the water quality of the upstream monitoring points on the downstream water quality.Meanwhile,existing models mostly adjust the hyperparameters of neural networks based on the particle swarm optimization algorithm in heuristic optimization algorithms.However,the optimization algorithm still needs to set many super parameters,and improper parameter selection can easily make the model fall into local optimization.A spatio-temporal water quality prediction model(WT-CNN-LSTM-HHO)is established,Harris Hawk optimization algorithm(HHO)is used to predict downstream water quality indexes of nitrogen,phosphorus and dissolved oxygen based on upstream water quality data.The experimental results show that the proposed model can significantly improve the performance of water quality prediction,and can set fewer super-parameters and achieve higher water quality prediction accuracy.关键词
时空水质预测/哈里斯鹰优化算法/LSTM神经网络/时间序列/CNN-LSTM/小波降噪Key words
water quality prediction/HHO/LSTM neural network/time series/CNN-LSTM/wavelet denoising分类
信息技术与安全科学引用本文复制引用
李顺勇,张睿轩,谭红叶..基于HHO优化的时空水质预测模型[J].现代电子技术,2024,47(2):176-182,7.基金项目
国家自然科学基金项目(82274360) （82274360）
国家自然科学基金项目(61976128) （61976128）
2022年度山西省研究生教育教学改革课题(2022YJJG010) （2022YJJG010）
