Open Access
Subscription Access
GLACIER LAKE OUTBURST FLOODS - PREDICTION USING XGBOOST
Abstract
The Glacial Lake Outburst Floods (GLOFs) - prediction using XGBoost is an advanced early detection platform aimed at reducing the risks associated with GLOFs, which are becoming more frequent due to climate change. Glacial lake outburst floods (GLOFs) occur when a glacial lake overflows its natural boundaries, releasing large amounts of water that can cause significant damage downstream. While global temperatures rise, the area of these lakes is increasing, which, in turn, increases the chances of such events happening and poses harm to human settlements and infrastructure, as well as ecosystems? It does so by monitoring the size of the lakes, water levels, temperatures, and the state of natural dam structures. By combining real-time data with algorithms, the system can forecast potential damage. With this, authorities can take measures in advance and mitigate the damage. In addition, the system provides valuable information for forecasting, easterly infrastructure services, and resource saving. This assists governments and responders in flood control planning and realization of social measures. By continuous monitoring and studying the situation, the GLOF warning system increases disaster preparedness and shifts the focus to protecting people and minimizing flood consequences.
References
W. W. Immerzeel, L. P. H. van Beek and M. F. P. Bierkens, ”Climate change will affect the Asian water towers,” in Science, 2010, doi: 10.1126/science.1183188.
A. Johns-Putra, ”Climate change in literature and literary studies: From cli-fi, climate change theater and ecopoetry to ecocriticism and climate change criticism,” in Wiley Interdisciplinary Reviews: Climate Change, 2016, doi: 10.1002/wcc.385.
W. R. L. Anderegg and G. R. Goldsmith, ”Public interest in climate change over the past decade and the effects of the ’climategate’ media event,” in Environmental Research Letters, vol. 9, no. 5, pp. 054005, 2014, doi: 10.1088/1748-9326/9/5/054005.
M. Rankl, C. Kienholz and M. Braun, ”Glacier changes in the Karakoram region mapped by multimission satellite imagery,” in The Cryosphere, vol. 8, pp. 977-989, 2014, doi: 10.5194/tc-8-977-2014.
J. C. Yde and N. T. Knudsen, ”20th-century glacier fluctuations on Disko Island (Qeqertarsuaq), Greenland,” in Annals of Glaciology, 2007, doi: 10.3189/172756407782871422.
L. Chen, T. Tan, L. Zhang and H. Huang, ”Seasonal Changes of Glacier Lakes in Tibetan Plateau Revealed by Multipolarization SAR Data,” in IEEE Transactions on Geoscience and Remote Sensing, 2021, doi: 10.1109/TGRS.2021.9506350.
Y. Li, C. Zhang, K. Zhang and W. Liu, ”A Glacial Lake Mapping Framework in High Mountain Areas: A Case Study of the Southeastern Tibetan Plateau,” in IEEE Transactions on Geoscience and Remote Sensing, 2024, doi: 10.1109/TGRS.2024.10036278.
X. Zhu, M. Wang, J. Li and T. Wang, ”A Progressive Learning Strategy for Large-Scale Glacier Mapping,” in IEEE Transactions on Geoscience and Remote Sensing, 2023, doi: 10.1109/TGRS.2023.9985678.
R. Patel, S. Bhattacharya and V. Kumar, ”Application of Inverse Map- ping for Automated Determination of Normalized Indices Useful for Land Surface Classification,” in IEEE Transactions on Geoscience and Remote Sensing, 2024, doi: 10.1109/TGRS.2024.10073902.
S. Ahmed, B. Singh and M. K. Verma, ”Mapping Glacial Lakes Using Historically Guided Segmentation Models,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, doi: 10.1109/JSTARS.2023.9924600.
Refbacks
- There are currently no refbacks.