文献类型：期刊文献

英文题名：A low-power water quality monitoring system and prediction model

作者：Li, Yangde[1]; Xie, Zaimi[2]; Mo, Chunmei[3]; Chen, Yuge[3]; Wang, Ji[3]

机构：[1] Planning and Design Institute Co, Consulting Department Guangdong Telecommunication, Zhanjiang, China; [2] School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, China; [3] School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China

年份：2022

外文期刊名：2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022

收录：EI(收录号：20225013233861)

基金：and time series characteristics, this paper uses PCA to screen key parameters affecting water quality, eliminate redundancy between variables, improve the K-means clustering algorithm for noise reduction of water quality data and network search method combined with the advantages of GRU network to learn water quality characteristics, and propose an improved K-GRU water quality accurate prediction model. The model performance indexes RMSE, MAPE, and R2 are 0.006, 0.017, and 98.56% respectively, compared with several other models, there is a large improvement in prediction accuracy and robustness, and the modeling study shows that the improved K-GRU model can make a reliable prediction of key parameters of offshore water quality. ACKNOWLEDGMENT We are very grateful to Guangdong Ocean University for providing the experimental base. This work was supported by the National Natural Science Foundation of China (51777046).

语种：英文

外文关键词：5G mobile communication systems - Aquaculture - Deep learning - Environmental technology - Forecasting - Informatization - K-means clustering - Learning algorithms - Learning systems - Search engines - Water quality

外文摘要：A low-power water quality monitoring system is proposed to promote the development of offshore aquaculture informatization and realize intelligent monitoring of the offshore aquaculture environments. The STM32F103C8T6 controller is used to collect information such as light, temperature, humidity, and pH at regular intervals, and transmit the data to the cloud monitoring platform through LoRa+5G technology to realize remote monitoring of multi-area environmental information. An improved K-GRU prediction model is established by combining deep learning and an improved K-means clustering algorithm. The model to PCA algorithm and K-means clustering algorithm to achieve the selection of water quality key parameters, and its data as the output of the network input module; based on this input to the noise reduction module for noise reduction processing, the noise reduction data input to the network learning module for training and learning, the use of cross-validation and network search method to optimize the network parameters and structure, the trained prediction model predicts the key parameters of water quality. The accuracy and reliability of the system's marine environmental information collection and the effectiveness of the water quality key parameter prediction models are verified by example. Compared with the traditional K-GRU prediction model, RMSE is reduced by 5.6%, MAPE is reduced by 10.9%, and R2 is improved by 0.94%, which can meet the practical needs of offshore aquaculture water quality monitoring and prediction. ? 2022 IEEE.