ItemConference Paper

Climate Change and Rainfall Variability in Goa: A Hybrid LSTM-Autoencoder based Predictive Approach

Title
Climate Change and Rainfall Variability in Goa: A Hybrid LSTM-Autoencoder based Predictive Approach
Creator
Kumar Chandar, S.
Description
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.
Source
Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.1597-1601
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
AE; deep learning; LSTM; Rain fall anomaly prediction
Coverage
Kumar Chandar S., Christ University, School of Business and Management, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155851-2;
Format
online
Language
English
Type
Conference paper

Citation

Kumar Chandar, S., “Climate Change and Rainfall Variability in Goa: A Hybrid LSTM-Autoencoder based Predictive Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25908.

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