Publication: Hazard classification using artificial neural network
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering::Electrical and electronic engineering | |
| dc.contributor.author | Muhammad Siraj Ahlami bin Zamri | |
| dc.date.accessioned | 2025-05-30T10:03:19Z | |
| dc.date.available | 2025-05-30T10:03:19Z | |
| dc.date.issued | 2024-08 | |
| dc.description.abstract | This research focuses on enhancing the landslide susceptibility mapping on Penang Island that targeting the electrical infrastructure using an Artificial Neural Network (ANN) model. The study integrates remote sensing data, Geographic Information Systems (GIS), and various normalization methods to create a comprehensive dataset of landslide conditioning factors including elevation, slope, aspect, curvature, rainfall, NDVI, and land use cover. By applying a frequency ratio method, the research identifies high-probability landslide areas. The samples were labelled as '1' for the landslide-prone regions and '0' for the non-landslide areas. The dataset of 26,294 samples is the result from balancing the 13,147 samples of the landslide occurrence data. Different normalization techniques such as mean-standard deviation and min-max are used to evaluate the ANN model's performance. The results show that the ANN model achieved a test accuracy of 86.66% with min-max normalization and 85.76% with mean-standard deviation normalization. This indicates that min-max normalization slightly outperforms mean-standard deviation normalization in this context. The results demonstrate the model's ability to accurately predict landslide-prone areas and providing valuable insights for risk mitigation and infrastructure protection. This approach underscores the importance of precise data preprocessing and balanced datasets in improving the reliability of landslide susceptibility models. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/22033 | |
| dc.language.iso | en | |
| dc.title | Hazard classification using artificial neural network | |
| dc.type | Resource Types::text::report::technical report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |