AI-driven anomaly detection for climate hazards in Sri Lanka using CNN-LSTM autoencoder models

Abstract

Floods, cyclones, and landslides, like environmental hazards, are recurring threats in Sri Lanka. They are increasing with climate variability. Conventional disaster forecasting systems are facing various limitations. Use of outdated technologies, lack of high-quality data, lack of geographic coverage, and insufficient integration of satellite images are major causes. This study proposes an unsupervised anomaly detection framework using a CNN + LSTM Autoencoder hybrid model to identify climate-related anomalies before disasters occur. The proposed model integrates daily weather variables for Sri Lanka obtained from Google Earth Engine and OCHA, together with satellite image brightness variables sourced from CIMSS. The model is trained solely on non-disaster sequences using an 8-day sliding window to capture temporal and spatial patterns. Therefore, the trained model is able to detect deviations from learned normalcy through reconstruction error, allowing early warnings without labelled disaster data. The initial evaluation yielded a moderate F1-score of 0.4,1 and the anomaly clusters aligned with known high-risk hazard areas. When the model is retrained on a rebalanced synthetic dataset, it helps improve accuracy to 70%. This enhanced disaster sensitivity. Finally, the retrained model was deployed in a Flask-based web app to enable anomaly predictions from 2018 to 2025 for public inference. This approach offers a data-efficient solution for location-based early warning in low-resource environments while addressing the gaps in public disaster datasets. Future research includes the use of attention mechanisms, late fusion multi-models, and integration of high-frequency satellite images to improve accuracy and model generalisation.