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基于ARIMA-IPOA-CNN-LSTM的太湖水体溶解氧浓度预测模型

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为了提高太湖水体中溶解氧浓度(DOC)参数的预测准确性,设计了一种基于 ARIMA-IPOA-CNN-LSTM的预测模型.首先,采用差分自回归移动平均模型(ARIMA)捕捉数据的时间序列趋势和季节性特征;其次,引入卷积神经网络(CNN)和长短期记忆网络(LSTM)模型,分别从数据中学习空间和时间特征;再次,提出了一种改进的鹈鹕优化算法(IPOA)来优化模型参数,算法增加了Logistic混沌映射种群初始化、反向差分进化、萤火虫扰动的方法,CEC2005函数的测试结果显著优于传统鹈鹕优化算法;最后,将"剪枝"模型部署于STM32嵌入式设备.试验结果表明,在溶解氧浓度预测方面,该模型具有高的准确性和鲁棒性,为水环境保护提供了一种高效、可靠的解决方案.
Prediction Model of Dissolved Oxygen Concentration in Taihu Lake Based on ARIMA-IPOA-CNN-LSTM
In order to improve the prediction accuracy of dissolved oxygen concentration(DOC)parameters in Taihu Lake,a prediction model based on ARIMA-IPOA-CNN-LSTM was designed.Firstly,the auto regressive integrated moving average(ARIMA)model was used to capture the time series trends and seasonal characteristics of the data.Sec-ondly,convolutional neural networks(CNN)and long short-term memory networks(LSTM)models were introduced to learn spatial and temporal features from the data,respectively.An improved pelican optimization algorithm(IPOA)was proposed to optimize model parameters.The proposed algorithm added methods such as Logistic chaotic mapping popula-tion initialization,reverse differential evolution,and firefly disturbance.The test results of the CEC2005 function were significantly better than those of traditional pelican optimization algorithms.Finally,the"pruning"model was deployed on the STM32 embedded device.Experimental results show that the model has high accuracy and robustness in predicting dissolved oxygen concentration,providing an efficient and reliable solution for water environment protection.

auto regressive integrated moving averagepelican optimization algorithmconvolutional neural net-workswater bodydissolved oxygen concentration

杨焕峥、崔业梅、徐玲、薛洪惠

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江苏省无线传感系统应用工程技术研究开发中心,江苏 无锡 214153

无锡商业职业技术学院,江苏 无锡 214153

常州大学微电子与控制工程学院,江苏 常州 213164

南京大学近代声学教育部重点实验室,江苏 南京 210093

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差分自回归移动平均 鹈鹕优化算法 卷积神经网络 水体 溶解氧浓度

国家自然科学基金项目中央高校基本科研业务费专项江苏高校"青蓝工程"项目

61873111020414380195RS20QL01

2024

10.20040/j.cnki.1000-7709.2024.20232063

水电能源科学
中国水力发电工程学会 华中科技大学 武汉国测三联水电设备有限公司

水电能源科学

CSTPCD北大核心
影响因子：0.525
ISSN：1000-7709
年,卷(期)：2024.42(10)
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