Anjali, P and Subramani, DN (2021) Inter and Intra-Annual Spatio-Temporal Variability of Habitat Suitability for Asian Elephants in India: A Random Forest Model-based Analysis. In: 2021 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2021, 6 - 10 December 2021, Virtual, Online, pp. 467-470.
|
PDF
InGARSS_2021.pdf - Published Version Download (4MB) | Preview |
Abstract
We develop a Random Forest model to estimate the species distribution of Asian elephants in India and study the inter and intra-annual spatiotemporal variability of habitats suitable for them. Climatic, topographic variables and satellite-derived Land Use/Land Cover (LULC), Net Primary Productivity (NPP), Leaf Area Index (LAI), and Normalized Difference Vegetation Index (NDVI) are used as predictors, and the species sighting data of Asian elephants from Global Biodiversity Information Reserve is used to develop the Random Forest model. A careful hyper-parameter tuning and training-validation-testing cycle are completed to identify the significant predictors and develop a final model that gives precision and recall of 0.78 and 0.77. The model is applied to estimate the spatial and temporal variability of suitable habitats. We observe that seasonal reduction in the suitable habitat may explain the migration patterns of Asian elephants and the increasing human-elephant conflict. Further, the total available suitable habitat area is observed to have reduced, which exacerbates the problem. This machine learning model is intended to serve as an input to the Agent-Based Model that we are building as part of our Artificial Intelligence-driven decision support tool to reduce human-wildlife conflict.
| Item Type: | Conference Paper |
|---|---|
| Publication: | 2021 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2021 - Proceedings |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Autonomous agents; Biodiversity; Computational methods; Decision support systems; Decision trees; Ecosystems; Land use; Machine learning; Population distribution, Artifi-cial intelligence for social good; Habitat suitability; Machine-learning; One-class Classification; Random forest modeling; Remote-sensing; Spatiotemporal variability; Species distribution modeling; Suitable habitat, Remote sensing |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 22 May 2023 07:29 |
| Last Modified: | 22 May 2023 07:29 |
| URI: | https://eprints.iisc.ac.in/id/eprint/81712 |
Actions (login required)
 |
View Item |
