AI-Driven Weather Prediction System with Real-Time Data Integration and Interactive Visualization

DOI

10.2991/978-94-6239-693-7_58How to use a DOI?

Keywords

Weather Prediction; Artificial Intelligence; ConvLSTM; Transformer Models; Cyclone Forecasting; Rapid Intensification; Interactive Visualization; Numerical Weather Prediction (NWP)

Abstract

Forecasters need to change their approach to weather prediction because extreme weather events keep increasing in number and strength throughout Southeast Asia which experiences extreme weather disasters. The implementation of physical-based Traditional Numerical Weather Prediction (NWP) models faces difficulties because their high computational requirements and operational delays create obstacles for issuing urgent weather alerts. The current paper presents a weather forecasting system based on artificial intelligence which addresses the existing challenges. The system uses advanced deep learning technologies to create weather prediction models that include Convolutional LSTMs (ConvLSTM) and transformers for forecasting cyclone paths and rain distribution and rapid intensification which represents the most dangerous weather phenomenon. The system uses Physics-Informed Neural Networks (PINNs) to implement atmospheric physical constraints which enhance system performance during extreme weather situations.

The implementation of Explainable AI (XAI) methods enables the model to provide clear visibility into its prediction process through the identification of critical weather elements that determine its forecasting outcomes. Our system will help make complicated meteorological data available and useful by using real-time data from various sources, including NOAA, ECMWF, and JAXA, and displaying the results on an interactive web-based visualization platform created with Next.js and Mapbox.

The suggested framework shows how AI can help reduce the prediction time by several folds and improve user interaction, which is consistent with important UN Sustainable Development Goals on climate action and disaster resilience.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Aryan Kundu
AU  - Trisita Silvia Debnath
AU  - C. Preethi
PY  - 2026
DA  - 2026/06/16
TI  - AI-Driven Weather Prediction System with Real-Time Data Integration and Interactive Visualization
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 587
EP  - 597
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_58
DO  - 10.2991/978-94-6239-693-7_58
ID  - Kundu2026
ER  -