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 Karthick, K.; Aruna, S.K.; Krishnan, S.; Ravivarman, S.; Manikandan, R. Title A name given to the resource A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Water Resources Management;Volume;40;Issue;8;Article No.;349; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11269-026-04725-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11269-026-04725-w</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039876487?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039876487?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 Karthick K., GMR Institute of Technology (GMRIT) (Deemed to be University), Andhra Pradesh, Rajam, 532 127, India; Aruna S.K., Department of AI and Data Science Engineering, School of Engineering and Technology, CHRIST (Deemed to be University) - Kengeri Campus, Bangalore, 560074, India; Krishnan S., Mahendra Engineering College, Tamil Nadu, Mallasamudram, Namakkal, 637503, India; Ravivarman S., Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Telangana, Hyderabad, 501218, India; Manikandan R., Department of ECE, Panimalar Engineering College, Chennai, 600123, India Description An account of the resource High-Impact Weather (HIW) events, particularly high-impact heavy rainfall, pose significant risks to urban infrastructure in Australia. Traditional forecasting approaches often struggle to resolve the complex, non-linear thermodynamic interactions that drive these infrequent events, while standard supervised machine learning models are hindered by severe class imbalance. This study presents a novel, multi-disciplinary framework that integrates synoptic climatology with unsupervised anomaly detection to classify and predict high-impact heavy rainfall events in Darwin, Sydney, Brisbane, and Perth. Using daily meteorological observations (20242025), we developed a multi-phase analytical framework comprising precursor, thermodynamic, kinematic, and system evolution phases to isolate the physical signatures of storm genesis. Exploratory analysis using Danger Rose polar histograms revealed a strong anisotropic risk pattern, with heavy rainfall predominantly associated with South-South-East (SSE) and West-South-West (WSW) vectors. Bivariate Kernel Density Estimation (KDE) revealed a distinct Thermodynamic Lock-in mechanism, where severe events are confined to narrow regimes of low pressure (&lt; 1010 hPa), high humidity (&gt; 60%), and compressed diurnal temperature ranges. To address the limited representation of severe events data (12.1%), we benchmarked five unsupervised anomaly detection algorithms. The results indicate that DBSCAN (Density-Based Spatial Clustering) yields the optimal performance (F1-Score: 0.319; Recall: 67.5%), significantly outperforming Isolation Forest and PCA. Topological validation via t-SNE projection confirms that high-impact heavy rainfall events form dense, cohesive clusters within the phase space rather than appearing as randomly distributed stochastic outliers. These findings prove that hybridizing physical phase-space analysis with density-based machine learning offers a robust pathway for early warning systems in data-sparse environments. The Author(s), under exclusive licence to Springer Nature B.V. 2026. Subject The topic of the resource Anomaly detection; DBSCAN; High-impact heavy rainfall; Meteorological precursors; Thermodynamic phase space; Wind-driven rain Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Relation A related resource ISSN: 9204741; 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