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 Conference Paper Faculty Publications- Conference Papers 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 Kumar Chandar, S. Title A name given to the resource Climate Change and Rainfall Variability in Goa: A Hybrid LSTM-Autoencoder based Predictive Approach 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 Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.1597-1601 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAUC68182.2026.11441140" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAUC68182.2026.11441140</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037430864?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037430864?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 Kumar Chandar S., Christ University, School of Business and Management, India Description An account of the resource Climate change has significantly altered precipitation patterns in coastal regions like Goa, India. Rainfall serves is a critical resource for crop cultivation in many developing countries. Accurate forecasting of rainfall is essential for sustainable planning, agriculture, and disaster mitigation. However, forecasting rainfall is still challenging due to the dynamic and non-linear nature of weather data. The intricate temporal correlations included into the data may be difficult for traditional time series models and machine learning techniques to adequately reach. This demands the use of advanced data-driven techniques capable of identifying these intricate patterns. This paper presents a data-driven approach using a Long Short-Term Memory Auto Encoder (LSTM-AE) to predict rainfall anomalies over Goa. Seven weather parameters are collected, preprocessed, and analyzed to train the LSTM-AE model. Efficacy of the model is assessed by computing MSE, MAE, and R2. Experimental results demonstrates that the proposed model exhibits strong predictive capability. This research contributes to enhancing early warning systems and developing adaptive climate strategies for the region. 2026 IEEE. Subject The topic of the resource AE; deep learning; LSTM; Rain fall anomaly prediction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155851-2; Language A language of the resource English Type The nature or genre of the resource Conference paper 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