i-manager Publications

Leveraging ConvLSTM and Satellite Imagery for Predictive Modeling of Floods, Landslides, and Earthquakes

Akash R.*, Mouli Krishna V.**, Varun Anto Priyans R.***, Vidhya V.****

*-**** Department of Artificial Intelligence and Data Science, Easwari Engineering College,Chennai, Tamil Nadu, India.

Periodicity:January - March'2025
DOI : https://doi.org/10.26634/jfet.20.2.21545

Abstract

This study combines the spatial data from satellite imagery with the temporal learning capabilities of convolutional long short-term memory (ConvLSTM) networks to improve both prediction accuracy and processing efficiency. By utilizing diverse spectral bands and resolutions, the model captures a wide range of environmental features. Preprocessing steps, such as normalization and noise reduction, are applied to refine the input data and enhance the performance of the ConvLSTM network. The architecture is carefully structured to balance spatial and temporal dependencies, ensuring the effective integration of satellite-derived data. The framework is optimized to identify complex relationships within the dataset, enabling precise forecasts of upcoming disasters. It has been tested on various natural events, including hurricanes, floods, and wildfires, achieving higher prediction accuracy and shorter lead times compared to traditional techniques. This integration of satellite imagery with ConvLSTM networks aims to strengthen early warning systems, improve disaster preparedness, and reduce economic and social damage to affected regions.

Keywords

Satellite Imagery, ConvLSTM, Natural Disaster Prediction, Spatial-Temporal Modeling, Machine Learning.

How to Cite this Article?

Akash, R., Krishna, V. M., Priyans, R. V. A., and Vidhya, V. (2025). Leveraging ConvLSTM and Satellite Imagery for Predictive Modeling of Floods, Landslides, and Earthquakes. i-manager’s Journal on Future Engineering & Technology, 20(2), 60-68. https://doi.org/10.26634/jfet.20.2.21545

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	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	15	15	200	15
Pdf & Online	35	35	400	25

Leveraging ConvLSTM and Satellite Imagery for Predictive Modeling of Floods, Landslides, and Earthquakes

Abstract

Keywords

How to Cite this Article?

References

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