#8160. Chronological Harris hawks-based deep LSTM classifier in wireless sensor network for aqua status prediction

October 2026publication date
Proposal available till 08-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Aquatic Science;
Ecology, Evolution, Behavior and Systematics;
Ecology;
Earth-Surface Processes;
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Abstract:
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.
Keywords:
aqua status prediction; convolutional concept; deep long short-term memory; Harris hawks optimization algorithm; Piecewise Fuzzy C-means clustering

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