International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning for Sustainable Water Resource Management: A Comprehensive Review |
|---|---|
| ABSTRACT | Recently, machine learning (ML) techniques have gained significant attention for their ability to analyze large datasets, identify hidden patterns, and support intelligent decision-making. In this context, modern strategies for managing water resources based on machine learning offer an effective, accurate, and sustainable solution. This paper provides a comprehensive review of ML algorithms and their applications in water resource management. In this paper, authors discuss in detail the theoretical background of ML algorithms, including methodology and performance metrics. The application section details various uses of ML in water resource management, including water demand forecasting, smart irrigation, leak detection, and water quality prediction. Furthermore, the paper discusses the current challenges, research gaps, and future prospects of ML-driven water conservation systems. |
| AUTHOR | SAVITA, RENU SHUKLA, MANMOHAN SINGH RAWAT Gurukula Kangri (Deemed to be University), Haridwar, Uttarakhand, India Uttarakahnd State Council for Science & Technology, Dehradun, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403075 |
| pdf/75_Machine Learning for Sustainable Water Resource Management A Comprehensive Review.pdf | |
| KEYWORDS | |
| References | 1. Ahmed, M. S., et al. (2020). Machine learning methods for water demand forecasting. Water Resources Management, 34(2), 1–15. 2. Al-Maolegi, M., & Arkok, B. (2014). An improved Apriori algorithm for association rules. International Journal of Natural Language Computing, 3, 21–29. 3. Babaeizadeh, M., Frosio, I., Tyree, S., Clemons, J., & Kautz, J. (2017). Reinforcement learning through asynchronous advantage actor-critic on a GPU. arXiv. https://arxiv.org/abs/1611.06256 4. Baher, S., & Lobo, L. M. (2012). A comparative study of association rule algorithms for course recommender system in e-learning. International Journal of Computer Applications, 39, 48–52. 5. Bhattacharjee, P., & Mitra, P. (2020). A survey of density based clustering algorithms. Frontiers of Computer Science, 15, 151308. 6. Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32. 7. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees. Wadsworth, Inc, Belmont California. 8. Carreira-Perpiñán, M. Á. (2015). A review of mean-shift algorithms for clustering. arXiv. 9. Chapelle, O., Schölkopf, B., & Zien, A. (2006). Semi-supervised learning. MIT Press. 10. Chen, T., Guestrin, C., 2016. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. New York, NY, USA. 11. Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn. 20, 273–297. 12. Cover, T.M., Hart, P.E., 1967. Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13, 21–27. 13. Deng, Z., Hu, Y., Zhu, M., Huang, X., & Du, B. (2014). A scalable and fast OPTICS for clustering trajectory big data. Cluster Computing, 18, 549–562. 14. Doe, J., & Smith, A. (2021). Smart irrigation systems using IoT and AI. IEEE Access, 8, 12345–12358 15. Domingos, P., Pazzani, M., 1997. On the optimality of the simple Bayesian classifer under zero-one loss. Mach. Learn. 29, 103–137. 16. Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V., 1997. Support vector regression machines. In: Mozer, M.C., Jordan, M.I., Petsche, T. (Eds.), Advances in Neural Information Processing Systems 9. MIT Press, Cambridge, MA, pp. 155–161. 17. Fong, A. (2010). Welcome message from the editor-in-chief. Journal of Advances in Information Technology, 1(1). 18. Fournier-Viger, P., Nkambou, R., & Tseng, V. S. M. (2011). RuleGrowth: Mining sequential rules common to several sequences by pattern-growth. In Proceedings of the ACM Symposium on Applied Computing (pp. 956–961). 19. François-Lavet, V., Fonteneau, R., & Ernst, D. (2016). How to discount deep reinforcement learning: Towards new dynamic strategies. arXiv. https://arxiv.org/abs/1512.02011 20. Frank, E., Trigg, L., Holmes, G., Witten, I.H., 2000. Naive bayes for regression. Mach. Learn. 41, 5–25. 21. Freund, Y., Schapire, R.E., 1995. A Decision-Theoretic Generalization of On-Line Learning and Application to Boosting. Lecture Notes in Computer Science. Springer berlin Heidelberg, pp. 23–37. 22. Friedman, J.H., 1989. Regularized discriminant analysis. J. Am. Stat. Assoc. 84, 165–175. 23. Friedman, J.H., 2001. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232. 24. Ghobadi, F., & Kang, D. (2022a). Improving long-term streamflow prediction in a poorly gauged basin using geo-spatiotemporal mesoscale data and attention-based deep learning: A comparative study. Journal of Hydrology, 615, 128542. https://doi.org/10.1016/j.jhydrol.2022.128542. 25. Ghobadi, F., & Kang, D. (2022b). Multi-step ahead probabilistic forecasting of daily streamflow using Bayesian deep learning: A multiple case study. Water, 14(22), 3672. https://doi.org/10.3390/w14223672 26. Girotra, M., Nagpal, K., Minocha, S., & Sharma, N. (2013). Comparative survey on association rule mining algorithms. International Journal of Computer Applications, 84, 18–22. 27. Gupta, A., et al. (2021). AI-based smart water management systems. Sustainable Cities and Society, 67. 28. Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., & Abbeel, P. (2019). Soft actor-critic algorithms and applications. arXiv. https://arxiv.org/abs/1812.05905 29. Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. 30. Hastie, T., Tibshirani, R., Friedman, J.H., 2009. Chapter 10. Boosting and additive tress. The Elements of Statistical Learning, second ed. Springer, New York, pp. 337–384. 31. Hsu, K., Levine, S., & Finn, C. (2019). Unsupervised learning via meta-learning. arXiv. https://arxiv.org/abs/1906.05426 32. Kim, M., Han, D. K., Park, J. H., & Kim, J. S. (2020). Motion planning of robot manipulators for a smoother path using a twin delayed deep deterministic policy gradient with hindsight experience replay. Applied Sciences, 10(2), 575. 33. Kodinariya, T., & Makwana, P. (2013). Review on determining of cluster in K-means clustering. International Journal of Advanced Research in Computer Science and Management Studies, 1, 90–95. 34. Kotsiantis, S., & Kanellopoulos, D. (2006). Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering, 32, 71–82. 35. Kumar, H., Koppel, A., & Ribeiro, A. (2023). On the sample complexity of actor-critic method for reinforcement learning with function approximation. Machine Learning, 112, 2433–2467. 36. Kumar, K. M., & Reddy, A. R. M. (2016). A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method. Pattern Recognition, 58, 39–48. 37. Kumar, R., et al. (2020). Leak detection in water distribution systems using machine learning. Journal of Hydroinformatics, 22(3), 456–470. 38. Lazaric, A., Restelli, M., & Bonarini, A. (2007). Reinforcement learning in continuous action spaces through sequential Monte Carlo methods. In Advances in Neural Information Processing Systems. 39. Liu, B., Hsu, W., & Ma, Y. (1999). Mining association rules with multiple minimum supports. In Proceedings of the Knowledge Discovery and Data Mining Conference (pp. 337–341). San Diego, CA, USA. 40. Liu, J., Cai, D., & He, X. (2010). Gaussian mixture model with local consistency. Proceedings of the AAAI Conference on Artificial Intelligence, 24, 512–517. 41. Miani, R. G. L., & Junior, E. R. H. (2018). Eliminating redundant and irrelevant association rules in large knowledge bases. In Proceedings of the 20th International Conference on Enterprise Information Systems (pp. 17–28). Funchal, Madeira, Portugal. 42. Mooney, C. H., & Roddick, J. F. (2013). Sequential pattern mining: Approaches and algorithms. ACM Computing Surveys, 45, 1–39. 43. Müllner, D. (2011). Modern hierarchical, agglomerative clustering algorithms. arXiv. 44. Said, A. M. (2009). A comparative study of FP-growth variations. International Journal of Computer Science and Network Security, 9, 266–272. 45. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv. https://arxiv.org/abs/1707.06347 46. Steinwart, I., Christmann, A., 2008. Support Vector Machines. Springer. 47. Sutton, R. S., McAllester, D., Singh, S., & Mansour, Y. (1999). Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems (pp. 1057–1063). MIT Press. 48. Tang, C. Y., Liu, C. H., Chen, W. K., & You, S. D. (2020). Implementing action mask in proximal policy optimization (PPO) algorithm. ICT Express, 6(3), 200–203. 49. Taylor, M. E., Whiteson, S., & Stone, P. (2006). Comparing evolutionary and temporal difference methods in a reinforcement learning domain. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1321–1328). 50. Triguero, I., García, S., & Herrera, F. (2015). Self-labeled techniques for semi-supervised learning: Taxonomy, software, and empirical study. Knowledge and Information Systems, 42(2), 245–284. https://doi.org/10.1007/s10115-013-0706-y 51. UNESCO (2023). World Water Development Report. Paris: UNESCO Publishing. 52. Van Hasselt, H., Guez, A., & Silver, D. (2016). Deep reinforcement learning with double Q-learning. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1), 2094–2100. 53. Vanham, D., Alfieri, L., Flörke, M., Grimaldi, S., Lorini, V., De Roo, A., & Feyen, L. (2021). The number of people exposed to water stress in relation to how much water is reserved for the environment: A global modelling study. The Lancet Planetary Health, 5(11), e766–e774. https://doi.org/10.1016/S2542-5196(21)00234-5. 54. Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17, 395–416. 55. Yoo, H., Kim, B., Kim, J. W., & Lee, J. H. (2020). Reinforcement learning based optimal control of batch processes using Monte-Carlo deep deterministic policy gradient with phase segmentation. Computers & Chemical Engineering, 144, 107133. 56. Zhang, S., et al. (2020). Deep learning for water quality prediction. Environmental Monitoring and Assessment, 192(5), 1–18. 57. Zhao, Y., & Karypis, G. (2002). Evaluation of hierarchical clustering algorithms for document datasets. In Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM ’02) (pp. 515–524). New York, NY, USA. 58. Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation (Technical Report CMU-CALD-02-107). Carnegie Mellon University, School of Computer Science. 59. Zhu, X., & Goldberg, A. B. (2009). Introduction to semi-supervised learning. Morgan & Claypool Publishers. |
