| dc.description.abstract | In the context of natural disasters, landslides
take more significance with the threat to life and property.
The uncertainty of the occurrence of landslides, and the
scarcity of accurate models for precise predictions have led
to huge losses. Studies have been conducted in this
paradigm worldwide to provide both quantitative and
qualitative analysis. Moreover, many approaches have
been taken to develop ML-based quantitative models. Yet,
there is a timely need to develop an explainable AI model
to predict and interpret landslides with logical arguments
comprehensively. This study is focused on developing an AI
model incorporating both training and logic to predict
landslides which can be used to map landslide
susceptibility. Landslide data, including topographical,
climatic, and geological factors affecting landslides,
related to Sri Lanka, retrieved from National Building
Research Organization (NBRO) of Sri Lanka, which
includes 3,000 data points 1,500 representing landslide
occurrences and 1,500 representing non-occurrence. This
research incorporates 25 features in modelling an artificial
neural network (ANN) which learns by training to give
predictions. Comparatively higher accuracy was obtained
achieving a test accuracy of 94.45%, a precision of
88.46%, and a recall of 98.17%, demonstrating superior
accuracy compared to existing models. In addition, a
logical/rule-based model based on Expert Systems and
Fuzzy Reasoning will be incorporated into the ANN model
to predict the landslide occurrence giving probabilities and
reasoning. Finally, a Geospatial framework will be
developed to simulate landslide susceptibility. The
approach will lead to mitigating the drawbacks of existing
early warning systems and present the general public with
a logical and more accurate mapping of landslide
susceptibility and minimize losses to life and property. The
findings of this research indicate the effectiveness of AI in
predicting landslide-prone areas, potentially reducing the
risk and impact of landslides in the study area | en_US |
