Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sherly, K.; Veeresha, Pundikala Title A name given to the resource Fractional operator-based mathematical model for hydrological cycle analysis with machine learning integration Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Modeling Earth Systems and Environment;Volume;11;Issue;3;Article No.;168; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s40808-025-02335-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40808-025-02335-w</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000032377?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000032377?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sherly K., CHRIST University, Bengaluru, India; Veeresha P., CHRIST University, Bengaluru, India Description An account of the resource The most important natural resource for maintaining ecosystems, life, and human civilization is water. Climate patterns, hydrological processes, and energy balance are all impacted by the constant movement of water across different parts of the Earths climate system. A new mathematical model is proposed using a fractional order, and this study investigates the four main elements of the hydrological cycle: atmospheric water, rainfall, surface water, and groundwater. The model uses the Caputo fractional operator to account for memory effects and long-term dependencies in water dynamics. A thorough qualitative and quantitative study examines the systems boundedness, stability, existence, and uniqueness. The AdamsBashforthMoulton (ABM) approach is used for numerical simulations, and it shows improved accuracy, stability, and reduced error metrics compared to traditional methods. Furthermore, bifurcation analysis reveals the systems possible behavior. Data-driven parameter estimation and trend forecasting are achieved by integrating Machine Learning (ML) techniques like the random forest regressor to improve predictive capabilities. Visualization tools such as pair plots, box plots, bar plots, and correlation matrix examines the associations between variables. The suggested method provides a strong framework for hydrological cycle modeling, increasing forecasting accuracy for water resource dynamics and climate-driven hydrological changes. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. Subject The topic of the resource AdamsBashforthMoulton method; Bifurcation analysis; Caputo fractional operator; Hydrological cycle model; Machine learning; Stability Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23636203; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online