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JCSIS

Long Short-Term Memory Network for Water Quality Parameter Forecasting in Coastal Desalination Intake Systems.

Lima Hongou
Volume 5

Abstract

Coastal desalination intake systems are highly sensitive to variations in seawater quality, as changes in physical, chemical, and biological parameters can directly affect pretreatment efficiency, membrane performance, energy consumption, fouling potential, and overall desalination plant reliability. Parameters such as temperature, salinity, turbidity, pH, dissolved oxygen, conductivity, chlorophyll-a concentration, total suspended solids, organic matter, and algal bloom indicators often fluctuate due to tidal cycles, seasonal changes, rainfall events, coastal currents, industrial discharge, and biological activity. These dynamic variations make accurate water quality forecasting essential for proactive plant operation, chemical dosing optimization, intake management, and early warning of harmful conditions. To address these challenges, this paper proposes a Long Short-Term Memory network for forecasting water quality parameters in coastal desalination intake systems.

The proposed framework utilizes historical time-series measurements collected from coastal intake monitoring stations, online water quality sensors, meteorological records, and oceanographic observations. Input variables include seawater temperature, salinity, turbidity, pH, conductivity, dissolved oxygen, oxidation-reduction potential, chlorophyll-a, total suspended solids, tide level, wave height, rainfall, wind speed, and previous intake water quality trends. A comprehensive preprocessing pipeline is applied to handle missing sensor readings, remove outliers, smooth noisy measurements, normalize variables, align multi-source data streams, and construct sliding-window sequences suitable for temporal forecasting. The LSTM architecture is employed to capture both short-term fluctuations and long-term dependencies in water quality behavior, enabling the model to learn recurring temporal patterns associated with tidal movement, seasonal variation, storm events, and biological activity.

The proposed model is designed to generate short-term and multi-step forecasts for key intake water quality parameters that influence desalination performance. Forecasting accuracy is evaluated using standard regression metrics such as Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, coefficient of determination, and forecasting skill score. Comparative analysis is conducted against persistence models, ARIMA, support vector regression, random forest, gradient boosting models, standard recurrent neural networks, and gated recurrent unit networks. Experimental evaluation is expected to demonstrate that the LSTM-based model provides improved forecasting accuracy by effectively modeling nonlinear temporal dependencies and delayed environmental effects in coastal water quality dynamics.

The forecasting outputs can be integrated into desalination plant decision-support systems to optimize pretreatment operation, adjust chemical dosing, schedule membrane cleaning, manage intake depth or location, and issue early warnings for high turbidity events, salinity shocks, organic loading, or algal bloom risks. By providing timely and reliable predictions of intake water quality, the proposed LSTM framework can reduce operational uncertainty, minimize membrane fouling, improve energy efficiency, enhance process stability, and support sustainable operation of coastal desalination facilities.

 

 

Keywords

References

  • [1] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [2] Lopes, J. M., Pinho, C. S., Martins, R. C., & Bandeira, T. (2026). Sustainability Gets Smarter: Competitive Pressure and AI L eading the
  • [3] Radwan, M., Ibrahim, A., Abdelsalam, M. M., Alhussan, A. A., Mattar, E. A., & El -Kenawy, E. S. M. (2026). Optimizing solar and wind
  • [4] Alhussan, A. A., El -Kenawy, E. S. M., Eid, M. M., & Khodadadi, N. (2026). Hybrid Al -Biruni and Puma Optimization (BERPO) for
  • [5] El-Kenawy, E. S. M., Ibrahim, A., Alhussan, A. A., Khafaga, D. S., Ahmed, A. E., & Eid, M. M. (2026). Smart city electricity loa d
  • [6] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Zaki, A. M., Ibrahim, A., Alhussan, A. A., ... & Eid, M. M. (2026). Glider sn ake
  • [7] Mekaret, F., Rabehi, A., Zebentout, B., Tizi, S., Douara, A., Bellucci, S., ... & Alhussan, A. A. (2024). A comparative study of Schottky
  • [8] KOUADRI, Ali, RABEHI, Abdelhalim, BENZIANE, Ali, et al. A Robust Multi -Transform Watermarking Scheme for Medical Images
  • [9] Tibermacine, I. E., Russo, S., Scarano, G., Tedesco, G., Rabehi, A., Alhussan, A. A., ... & Napoli, C. (2025). Conditional VA E for
  • [10] Kouadri, A., Benziane, A., Rabehi, A., Rabehi, A., Alhussan, A. A., Khafaga, D. S., & El -Kenawy, E. S. M. (2025). A novel hybrid
  • [11] Russo, S., Tibermacine, I. E., Randieri, C., Rabehi, A., Alharbi, A. H., El -Kenawy, E. S. M., & Napoli, C. (2025). Exploiting facial
  • [12] Bentegri, H., Rabehi, M., Kherfane, S., Nahool, T. A., Rabehi, A., Guermoui, M., ... & El -Kenawy, E. S. M. (2025). Assessment of
  • [13] Tibermacine, I. E., Russo, S., Citeroni, F., Mancini, G., Rabehi, A., Alharbi, A. H., ... & Napoli, C. (2025). Adversarial de noising of EEG
  • [14] Ouahabi, M. S., Benyounes, A., Barkat, S., Ihammouchen, S., Rekioua, T., Rabehi, A., ... & Alharbi, A. H. (2025). Real -time sensor fault
  • [15] Belaid, A., Guermoui, M., Khelifi, R., Arrif, T., Chekifi, T., Rabehi, A., ... & Alhussan, A. A. (2024). Assessing suitable a reas for PV
  • [16] Mehallou, A., M’hamdi, B., Amari, A., Teguar, M., Rabehi, A., Guermoui, M., ... & Khafaga, D. S. (2025). Optimal multiobjecti ve
  • [17] Rabehi, A., El -Hadi, M., Benmahmoud, S., Rabehi, A., Alharbi, A. H., & El -Kenawy, E. S. M. (2025). SOCA -CFAR Processor in A
  • [18] Bakria, D., Beladel, A., Korich, B., Teta, A., Mohammedi, R. D., Laouid, A. A., ... & El -kenawy, E. S. (2025). A novel enhanced Grey
  • [19] Mishra, P., Alhussan, A. A., Khafaga, D. S., Lal, P., Ray, S., Abotaleb, M., ... & El -Kenawy, E. S. M. (2024). Forecasting production of
  • [20] El-Kenawy, E. S. M., Mirjalili, S., Abdelhamid, A. A., Ibrahim, A., Khodadadi, N., & Eid, M. M. (2022). Meta -heuristic optimization
  • [21] Abdelhamid, A. A., El -Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., ... & Saber, M. (2022).
  • [22] Alharbi, A. H., Towfek, S. K., Abdelhamid, A. A., Ibrahim, A., Eid, M. M., & Khafaga, D. S. & Saber, M.(2023). Diagnosis of
  • [23] Alhussan, A. A., & Towfek, S. K. (2024). 5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algori thm.
  • [24] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [25] Abdelhamid, A. A., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Eed, M. (2024). Potato harvesting pred iction
  • [26] Eed, M., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Arnous, R. (2024). Potato Consumption Forecastin g
  • [27] Mahmood, S., Sun, H., Iqbal, A., Alhussan, A. A., & El -kenawy, E. S. M. (2024). Green finance, sustainable infrastructure, and green
  • [28] El-Kenawy, E. S. M., Alhussan, A. A., Khodadadi, N., Mirjalili, S., & Eid, M. M. (2024). Predicting potato crop yield with machi ne
  • [29] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [30] Ghasemi, M., Khodadadi, N., Trojovský, P., Li, L., Mansor, Z., Abualigah, L., ... & El -Kenawy, E. S. M. (2025). Kirchhoff’s law
  • [31] Yassen, M. A., El -Kenawy, E. S. M., Abdel -Fattah, M. G., Ismail, I., & Mostafa, H. E. D. S. (2025). Explainable artificial intelligence
  • [32] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [33] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [34] El-kenawy, E. S. M., Alhussan, A. A., Mattar, E. A., & Radwan, M. (2026). Feature selection and hyperparameter tuning in transfo rmer -
  • [35] Chen, L., Xu, C., Lim, W. H., Sharma, A., Tiang, S. S., Chong, K. S., ... & Khafaga, D. S. (2025). Transparent and reliable c onstruction
  • [36] Khodadadi, N., Towfek, S. K., Zaki, A. M., Alharbi, A. H., Khodadadi, E., Khafaga, D. S., ... & Eid, M. M. (2025). Predicting
  • [37] Alhussan, A. A., El -Kenawy, E. S. M., Khafaga, D. S., Alharbi, A. H., & Eid, M. M. (2025). Groundwater resource prediction and
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