ItemArticle

A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments

Title
A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments
Creator
Karthick, K.; Aruna, S.K.; Krishnan, S.; Ravivarman, S.; Manikandan, R.
Description
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 (< 1010 hPa), high humidity (> 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.
Source
Water Resources Management;Volume;40;Issue;8;Article No.;349;
Date
01-01-2026
Publisher
Springer Science and Business Media B.V.
Subject
Anomaly detection; DBSCAN; High-impact heavy rainfall; Meteorological precursors; Thermodynamic phase space; Wind-driven rain
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 9204741;
Format
online
Language
English
Type
Article

Citation

Karthick, K.; Aruna, S.K.; Krishnan, S.; Ravivarman, S.; Manikandan, R., “A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/21961.

Output Formats