In order to effectively solve the problem of low accuracy of seawater water quality prediction, an optimized water quality parameter prediction model is constructed in this paper. The model first screened the key factors of water quality data with the principal component analysis (PCA) algorithm, then realized the de-noising of the key factors of water quality data with an ensemble empirical mode decomposition (EEMD) algorithm, and the data were input into the two-dimensional convolutional neural network (2D-CNN) module to extract features, which were used for training and learning by attention, gated recurrent unit, and an encoder–decoder (attention–GRU–encoder–decoder, attention–GED) integrated module. The trained prediction model was used to predict the content of key parameters of water quality. In this paper, the water quality data of six typical online monitoring stations from 2017 to 2021 were used to verify the proposed model. The experimental results show that, based on short-term series prediction, the root mean square error (RMSE), mean absolute percentage error (MAPE), and decision coefficient (R2) were 0.246, 0.307, and 97.80%, respectively. Based on the long-term series prediction, RMSE, MAPE, and R2 were 0.878, 0.594, and 92.23%, respectively, which were all better than the prediction model based on an enhanced clustering algorithm and adam with a radial basis function neural network (ECA–Adam–RBFNN), a prediction model based on a softplus extreme learning machine method with partial least squares and particle swarm optimization (PSO–SELM–PLS), and a wavelet transform-depth Bi–S–SRU (Bi-directional Stacked Simple Recurrent Unit) prediction model. The PCA–EEMD–CNN–attention–GED prediction model not only has high prediction accuracy but can also provide a decision-making basis for the water quality control and management of aquaculture in the waters around Zhanjiang Bay.
A wireless sensor network (WSN) monitoring system for saline-alkali-tolerant rice based on long-range + unmanned aerial vehicle (LoRa + UAV) has been proposed in this paper to meet the requirements of difficult deployment, low scalability, and poor network reliability of traditional farmland environmental monitoring systems. The system automatically forms a double-layer star topology network through the terminal node and central node (intranet), central node, and UAV mobile node (extranet). Data transmission is carried out through LoRa technology, routing planning is carried out for UAV mobile receiving nodes at different flight altitudes, and an early warning mechanism for UAV energy is added. It effectively prevents the UAV from destroying the communication capability of the external network due to energy exhaustion during operation. The system transmits information remotely to the server of the management center through a 5G network, which analyzes and processes the data and publishes it on the Web. The system has been tested in the field, and the reliable communication range of the node is 1∼4 km under the saline-alkali-tolerant rice environment, and the service life of the terminal node can reach more than 3 months. During continuous operation, the maximum packet loss rate of the internal and external integrated networks is 1.45%. The system can stably and real-time monitor saline-alkali-tolerant rice environmental information, and the error rate is less than 10%, which can meet the performance requirements of the saline-alkali-tolerant rice environmental monitoring systems, such as low power consumption, wide coverage, stability, and reliability, and has a good engineering significance.
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