Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment
Floods have been identified as one of the world's most common and widely distributed natural disasters over the last few decades. Floods' negative impacts could be significantly reduced if accurately predicted or forecasted in advance. Apart from large-scale spatiotemporal data and greater...
Saved in:
| Main Authors: | Vijendra Kumar, Kul Vaibhav Sharma, Nikunj K. Mangukiya, Deepak Kumar Tiwari, Preeti Vijay Ramkar, Upaka Rathnayake |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2025-01-01
|
| Series: | AIMS Environmental Science |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/environsci.2025004 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Unravelling flood risk in the Rel River watershed, Gujarat using coupled earth observations, multi criteria decision making and Google Earth Engine
by: Keval H. Jodhani, et al.
Published: (2024-12-01) -
Spatio-Temporal Variation in Pluvial Flash Flood Risk in the Lhasa River Basin, 1991–2020
by: Xiaoran Fu, et al.
Published: (2024-10-01) -
Empowering flood preparedness: Enhancing flood knowledge, risk perception, and preparedness among primary school learners in flood-affected southern Thailand
by: Mujalin Intaramuean, et al.
Published: (2025-04-01) -
Hazard assessment and early warnings of flood disasters in the Yangtze-Huaihe river basin
by: Jiali Shao, et al.
Published: (2025-12-01) -
Risk perception of natural hazards of the inhabitants of Pico de Tancítaro, Michoacán, México
by: Gemma Gómez Castillo, et al.
Published: (2023-12-01)