Analyzing the Role of Fusing Image and Time Series Data in Forecasting Rainfall-Induced Landslides

Sawant, Siddhi (2024) Analyzing the Role of Fusing Image and Time Series Data in Forecasting Rainfall-Induced Landslides. Masters thesis, Dublin, National College of Ireland.

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Abstract

Landslides are a widespread natural peril triggering remarkable destruction. The landslides induced by rainfall arise in all highland zones, triggering living beings and nature to be disarrayed. Global warming and ensuing changes in climate patterns have radically transformed the environment, resulting in a substantial increase in rainfall. Landslides and historical rainfall have a strong correlation. In today’s world of information and Communication Technologies (ICT), natural catastrophes can be regulated effectively. The study proposes a multi-modal feature fusion, based on intermediate fusion approach wherein the features, respective to each modality are fused before classification. Two diverse data modalities and their respective models are fitted to build a multi-modal model. Deep learning sequential and computer vision models, Long Short-term Memory and Bidirectional Long Short-Term Memory based on the performance are selected to build a multi-modal fusion model to predict rainfall-induced landslides. The multi-modal is trained and validated using rainfall time-series data and landslide image dataset. The result obtained depicts the fusion approach is effective in predicting rainfall-induced landslides.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Shahid, Abdul UNSPECIFIED
Uncontrolled Keywords:	Rainfall-Induced Landslides; Intermediate fusion; LSTM; Bi-directional LSTM; CNN-ResNet50v2
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Artificial Intelligence
Depositing User:	Ciara O'Brien
Date Deposited:	20 Jun 2025 10:27
Last Modified:	20 Jun 2025 10:27
URI:	https://norma.ncirl.ie/id/eprint/7966

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