## 6 Conclusions  
This paper proposed a novel integrated method for predicting deep learning in UWSNs. Also, it presented a new embedded system of transceiver modules in UWSN design, and a distributed method was the behaviour of deployment in the designated network. Indeed, we conducted our experiments in government fish farms that encompassed underwater wireless sensor network integration with positioning systems under IoT. Farming sensors have specifications gained from the International Resource Identifiers System (IRIS) requirements. Furthermore, a communication modem M64a transceiver, ESP8266-01 Wi-Fi module, and positioning system Underwater GPS G2 were used. The M64a transceiver is designed to operate in low-power, low-bitrate, and long-range modes. M64a provides a dependable acoustic communication link between two subsea locations with limited space, weight, or power. However, sensors must be encased in materials that can withstand high pressure, corrosion, and biofouling. Long-lasting batteries or energy harvesting methods (e.g., from underwater currents) are essential due to the difficulty of replacing power sources underwater.  
Throughout deployments, the data were collected from each sensor node in the UWSN clusters. Then, the gathered data were processed synchronically via a series of the proposed deep filtering-learning models. To improve signal emissions, cancel any interferences accompanied by the emitted signal, and gain the soft pure signal. The practical experiments revealed that water quality is heavily influenced by pH and increased water turbidity. So, the pH factor was used to determine the appropriateness of the water. Further, the main control unit calculates a variety of indicators to ensure the purity of outgoing signals that assure water quality and a favourable environment for fish growth. The proposed method enhanced the performance of a variety of sensors in challenging or unpredictable underwater circumstances. Multiple experiments on various datasets pointed to the proposed deep learning-based prediction model having high accuracy in prediction by lost events, an optimal filtering statistic for the transferred signals, and a positive alarm rate of any anomaly. However, deploying underwater sensors is a difficult undertaking that necessitates meticulous preparation and consideration of the particular obstacles presented by the underwater environment. Thus, deploying several sensors and providing redundancy can aid in data integrity and system reliability. To achieve long-term stability in UWSNs, equipment must be robust to withstand harsh underwater conditions, including high pressure, salinity, and potential biofouling; regular maintenance and checks are necessary to ensure long-term functionality and reliability. Cloud-based software can facilitate the sharing, viewing, and comparison of underwater missions, enhancing the long-term stability of data management, and regular maintenance or self-cleaning mechanisms can help prevent biofouling, which can degrade sensor performance over time.  
Conceptualization, W.M.E.; Methodology, W.M.E. and E.E.-S.; Software, E.E.-S.; Validation, M.I.I; Formal analysis, M.A.; Data curation, M.I.I.; Writing--original draft, M.A. and E.E.-S.; Writing--review & editing, M.I.I.; Supervision, W.M.E.; Project administration, W.M.E.; Funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Researchers Supporting Project number (RSPD2024R636), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare that they have no conflicts of interest to report regarding the present study.