Year: 2025
Type: article
Abstract: Increasing frequency and intensity of extreme weather events due to climate change necessitate accurate predictive models for mitigation and adaptation. This paper proposes a novel deep learning (DL) framework integrating Convolutional Long Short-Term Memory (ConvLSTM) networks and attention mechanisms for spatio-temporal prediction of extreme events (heatwaves, floods, hurricanes). Leveraging multi-source climate reanalysis data (ERA5, CMIP6 projections) and remote sensing imagery, the model captures complex non-linear patterns and teleconnections often missed by traditional Numerical Weather Prediction (NWP) and statistical methods. Evaluated on global datasets spanning 1980-2023, our approach reduces Root Mean Squared Error (RMSE) by 32% for heatwave intensity prediction and improves hurricane trajectory accuracy by 28% compared to ECMWF-IFS benchmarks. The model demonstrates robust skill in 2050 climate projections under RCP 8.5, highlighting its potential for climate resilience planning. Implementation challenges and scalability solutions are discussed.
DOI: 10.64252/0s47f685
Authors: Dr.T. Vengatesh,, Dr.R. Ramya,, Viji.M, Saint Jesudoss.S,, Jakkapu Nagalakshmidevi, Dr. Prabhat Kumar, D. Nagaraj, D. Vanathi
Institutions: Aditya University, COER University
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W4413520698