Aquaculture becomes very popular in economic where aquatic organisms, like fishes and prawns, are mainly dependent on the quality of water in aquaculture pond. Also, the water quality constraints, which include turbidity, carbon dioxide, temperature, pH level, dissolved oxygen and phosphorus, are considered for achieving better performance. Hence, this paper presents an approach for aqua status prediction based on Deep Long Short-Term Memory (Deep LSTM) classifier. The sensor nodes are placed in the aqua pond for measuring the parameters of water quality, and then the cell network transformation is done using the Voronoi partition. After that, the Cluster Head (CH) selection is carried out using Piecewise Fuzzy C-means clustering (piFCM). Once the clusters are selected, the Chronological Harris Hawks (Chronological HH) optimization algorithm is introduced for optimal sink placement where the constraints for enabling the optimal sink placement are the distance and energy of the nodes. Finally, the aqua status is predicted using Deep LSTM. The performance of the Chronological HH-based Deep LSTM is computed in terms of accuracy, energy and the number of dead nodes. The proposed Chronological HH-based Deep LSTM outperformed other methods with maximal accuracy of 92.65%, maximal energy of 0.976 and the minimal dead nodes of 32.

中文翻译：

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无线传感器网络中基于时间顺序 Harris hawks 的深度 LSTM 分类器，用于水状态预测

水产养殖在经济中变得非常流行，其中鱼类和对虾等水生生物主要依赖于水产养殖池塘的水质。此外，还考虑了水质限制，包括浊度、二氧化碳、温度、pH 值、溶解氧和磷，以实现更好的性能。因此，本文提出了一种基于深度长短期记忆（Deep LSTM）分类器的水状态预测方法。传感器节点放置在水池中用于测量水质参数，然后使用Voronoi分区进行细胞网络转换。之后，使用分段模糊 C 均值聚类 (piFCM) 进行簇头 (CH) 选择。一旦选择了集群，引入了 Chronological Harris Hawks (Chronological HH) 优化算法以实现最佳 sink 放置，其中启用最佳 sink 放置的约束是节点的距离和能量。最后，使用 Deep LSTM 预测水状态。基于 Chronological HH 的 Deep LSTM 的性能是根据精度、能量和死节点数量计算的。提出的基于 Chronological HH 的 Deep LSTM 的性能优于其他方法，最大准确度为 92.65%，最大能量为 0.976，最小死节点为 32。能量和死节点的数量。提出的基于 Chronological HH 的 Deep LSTM 的性能优于其他方法，最大准确度为 92.65%，最大能量为 0.976，最小死节点为 32。能量和死节点的数量。提出的基于 Chronological HH 的 Deep LSTM 的性能优于其他方法，最大准确度为 92.65%，最大能量为 0.976，最小死节点为 32。