Integrating Machine Learning and Trend Analysis for Rainfall Forecasting: Insights From <scp>DBSCAN</scp> , Spectral Clustering, and Climate Variability Assessments Over Major Cities in India

ABSTRACT Urban hydrological systems are increasingly vulnerable to rainfall variability and extreme precipitation events, necessitating robust analytical frameworks for spatial classification and predictive diagnostics. This study addresses a key gap in urban climate analytics by applying unsupervised machine learning models to classify rainfall patterns across six major Indian cities, Bangalore, Chennai, Delhi, Hyderabad, Kolkata, and Mumbai, each representing distinct climatic zones and monsoon regimes. Using gridded daily rainfall data from the India Meteorological Department (IMD), spanning 1981–2023 at a spatial resolution of 0.25° × 0.25°, the analysis reveals intensifying rainfall trends in Mumbai and Kolkata, consistent with urbanisation‐induced climatic perturbations. Notable extreme events in Chennai (2005, 2015, 2021) and Bangalore (2005, 2017, 2022) are linked to cyclonic activity and elevated atmospheric moisture fluxes. Six clustering models, DBSCAN, Spectral Clustering, K‐Means, OPTICS, Gaussian Mixture Model (GMM), and Agglomerative Clustering, were applied to evaluate spatial coherence and anomaly detection. DBSCAN achieved the highest silhouette score (0.82), followed by Spectral Clustering (0.80), K‐Means (0.70), and OPTICS (0.44; Accuracy: 0.06). GMM and Agglomerative Clustering provided comparative insights into probabilistic and hierarchical rainfall structures. Despite their utility in spatial classification, all models produced negative R 2 values in time‐series forecasting, reaffirming the limitations of clustering algorithms for temporal prediction tasks. Future work should explore hybrid deep learning approaches such as LSTM or ARIMA to better capture sequential dependencies and enhance operational forecasting. This study presents a scalable framework integrating climate trend diagnostics with AI‐based classification, contributing to improved urban rainfall pattern detection, flood risk assessment, and climate‐resilient planning.