Enhancing water quality prediction with artificial intelligence: A hybrid convlstm model for spatio-temporal analysis

Year 2025, Volume: 9 Issue: 2, 107 - 117, 20.08.2025

Esen Damla Balo Utku , Anıl Utku , Banu Kutlu

https://doi.org/10.35860/iarej.1679575

Abstract

Water quality plays a vital role in public health, environmental sustainability and ecosystem balance. However, industrialization, urbanization, and agricultural activities cause water pollution to increase, threatening both human health and aquatic ecosystems. Traditional water quality monitoring methods are usually time-consuming, costly, and require manual intervention. Therefore, developing automatic, data-driven, and high-accuracy prediction models is crucial for sustainable water management. This study created a hybrid ConvLSTM model to increase prediction accuracy. The created model was comparatively analyzed with RF, SVR, XGBoost, MLP, CNN, and LSTM. The dataset used includes historical measurement values of chemical pollutants. Input data includes geographical coordinates of sample points, chemical parameter type, and compliance with regulatory standards. Experimental results show that ConvLSTM provides the lowest prediction errors by learning spatial and temporal dependencies and reaches the highest accuracy rate with 0.994 R2 compared to other models.

Keywords

CNN , Deep learning , Environmental sustainability , LSTM , Water quality

References

• 1. Babuji, P., Thirumalaisamy, S., Duraisamy, K., Periyasamy, G. Human health risks due to exposure to water pollution: A review, Water, 2023. 15(14).
• 2. Darko, G., Obiri-Yeboah, S., Takyi, S. A., Amponsah, O., Borquaye, L. S., Amponsah, L. O., Fosu-Mensah, B. Y. Urbanizing with or without nature: Pollution effects of human activities on water quality of major rivers that drain the Kumasi Metropolis of Ghana, Environmental Monitoring and Assessment, 2022. 194(1).
• 3. Abbas, H. M. M., Rais, U., Altaf, M. M., Rasul, F., Shah, A., Tahir, A., Khan, M. N. Microbial-inoculated biochar for remediation of salt and heavy metal contaminated soils, Science of The Total Environment, 2024. 176104.
• 4. Singh, P. K., Kumar, U., Kumar, I., Dwivedi, A., Singh, P., Mishra, S., Sharma, R. K. Critical review on toxic contaminants in surface water ecosystem: sources, monitoring, and its impact on human health, Environmental Science and Pollution Research, 2024. 31(45), p. 56428-56462.
• 5. Sengorur, B., Koklu, R., Ates, A. Water quality assessment using artificial intelligence techniques: SOM and ANN—A case study of Melen River Turkey, Water Quality, Exposure and Health, 2015. 7, p. 469-490.
• 6. Zhang, Q., You, X. Y. Recent advances in surface water quality prediction using artificial intelligence models, Water Resources Management, 2024. 38(1): p. 235-250.
• 7. Liu, Z., Zhou, J., Yang, X., Zhao, Z., Lv, Y. Research on water resource modeling based on machine learning technologies, Water, 2024. 16(3).
• 8. Zhang, M., Zhang, Z., Wang, X., Liao, Z., Wang, L. The use of attention-enhanced CNN-LSTM models for multi-indicator and time-series predictions of surface water quality, Water Resources Management, 2024. 38: p. 1-17.
• 9. Miller, T., Durlik, I., Kostecka, E., Kozlovska, P., Łobodzińska, A., Sokołowska, S., Nowy, A. Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data, Electronics, 2025. 14(4).
• 10. Haq, K. R. A., Harigovindan, V. P. Water quality prediction for smart aquaculture using hybrid deep learning models, IEEE Access, 2022. 10: p. 60078-60098.
• 11. Zhang, H., Xue, B., Wang, G., Zhang, X., Zhang, Q. Deep learning-based water quality retrieval in an impounded lake using landsat 8 imagery: An application in Dongping lake, Remote Sensing, 2022. 14(18).
• 12. Qian, J., Liu, H., Qian, L., Bauer, J., Xue, X., Yu, G., Norra, S. Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir, Frontiers in Environmental Science, 2022. 10.
• 13. Yang, W., Fu, B., Li, S., Lao, Z., Deng, T., He, W., Chen, Z. Monitoring multi-water quality of internationally important karst wetland through deep learning, multi-sensor and multi-platform remote sensing images: A case study of Guilin, China, Ecological Indicators, 2023. 154.
• 14. Alshehri, F., Rahman, A. Coupling machine and deep learning with explainable artificial intelligence for improving prediction of groundwater quality and decision-making in Arid Region, Saudi Arabia, Water, 2023. 15(12).
• 15. Jongjaraunsuk, R., Taparhudee, W., Suwannasing, P. Comparison of water quality prediction for red tilapia aquaculture in an outdoor recirculation system using deep learning and a hybrid model, Water, 2024. 16(6).
• 16. Arepalli, P. G., Naik, K. J. Water contamination analysis in IoT enabled aquaculture using deep learning based AODEGRU, Ecological Informatics, 2024. 79.
• 17. Kandasamy, L., Mahendran, A., Sangaraju, S. H. V., Mathur, P., Faldu, S. V., Mazzara, M. Enhanced remote sensing and deep learning aided water quality detection in the Ganges River, India supporting monitoring of aquatic environments, Results in Engineering, 2025. 25.
• 18. Şen, Ö., Keser, S. B., Keskin, K. Early stage diabetes prediction using decision tree-based ensemble learning model, International Advanced Researches and Engineering Journal, 2023, 7(1): p. 62-71.
• 19. Salman, H. A., Kalakech, A., Steiti, A. Random forest algorithm overview, Babylonian Journal of Machine Learning, 2024. p. 69-79.
• 20. Bodapati, J. D., Balaji, B. B. Self-adaptive stacking ensemble approach with attention based deep neural network models for diabetic retinopathy severity prediction, Multimedia Tools and Applications, 2024. 83(1): p. 1083-1102.
• 21. Devasahayam, S., Albijanic, B. Predicting hydrogen production from co-gasification of biomass and plastics using tree based machine learning algorithms, Renewable Energy, 2024. 222.
• 22. Tanveer, M., Rajani, T., Rastogi, R., Shao, Y. H., Ganaie, M. A. Comprehensive review on twin support vector machines, Annals of Operations Research, 2024. 339(3): pp. 1223-1268.
• 23. Ngu, J. C. Y., Yeo, W. S., Thien, T. F., Nandong, J. A comprehensive overview of the applications of kernel functions and data-driven models in regression and classification tasks in the context of software sensors, Applied Soft Computing, 2024. 164.
• 24. Zhao, G., Pan, X., Yan, H., Tian, J., Han, Y., Guan, H. Predicting engineering properties of controlled low-strength material made from waste soil using optimized SVR models, Case Studies in Construction Materials, 2024. 20.
• 25. Guido, R., Ferrisi, S., Lofaro, D., Conforti, D. An overview on the advancements of support vector machine models in healthcare applications: a review, Information, 2024. 15(4).
• 26. Asselman, A., Khaldi, M., Aammou, S. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm, Interactive Learning Environments, 2023. 31(6): p. 3360-3379.
• 27. Bansal, M., Goyal, A., Choudhary, A. A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning, Decision Analytics Journal, 2022. 3.
• 28. Demir, S., Sahin, E. K. An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost, Neural Computing and Applications, 2023. 35(4): p. 3173-3190.
• 29. Sagi, O., Rokach, L. Approximating XGBoost with an interpretable decision tree, Information sciences, 2021. 572: p. 522-542.
• 30. Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Ghayvat, H. CNN variants for computer vision: History, architecture, application, challenges and future scope, Electronics, 2021. 10(20).
• 31. Mohammadpour, L., Ling, T. C., Liew, C. S., Aryanfar, A. A survey of CNN-based network intrusion detection, Applied Sciences, 2022. 12(16).
• 32. Nirthika, R., Manivannan, S., Ramanan, A., Wang, R. Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study, Neural Computing and Applications, 2022. 34(7): p. 5321-5347.
• 33. Kamalraj, R., Neelakandan, S., Kumar, M. R., Rao, V. C. S., Anand, R., Singh, H. Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm, Measurement, 2021. 183.
• 34. AlSaeed, D., Omar, S. F. Brain MRI analysis for Alzheimer’s disease diagnosis using CNN-based feature extraction and machine learning, Sensors, 2022. 22(8).
• 35. Şengül, F., Akkaya, S. A Modified MFCC-based deep Learning method for emotion classification from speech, International Advanced Researches and Engineering Journal, 2024, 8(1): p. 33-42.
• 36. Hakkı, L., Serbes, G. Detection of wheeze sounds in respiratory disorders: a deep Learning approach, International Advanced Researches and Engineering Journal, 2024, 8(1): p. 20-32.
• 37. Zhang, Y. Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches, Applied and Computational Engineering, 2023. 22: p. 218-226.
• 38. Ehteram, M., Nia, M. A., Panahi, F., Farrokhi, A. Read-First LSTM model: A new variant of long short term memory neural network for predicting solar radiation data, Energy Conversion and Management, 2024. 305.
• 39. Yang, L., Wang, S., Chen, X., Chen, W., Saad, O. M., Chen, Y. Deep-learning missing well-log prediction via long short-term memory network with attention-period mechanism, Geophysics, 2023. 88(1): p. 31-48.