Development and Application of a Real-Time GIS Digital Platform for Landslide Risk Analysis
Bibliographic record
Abstract
In the current global context of climate change, the frequency and intensity of disasters, including landslides, are on the rise.During the initial three quarters of 2023, a notable incidence of fatalities resulting from landslides was observed across several nations, with specific instances including India reporting 26 casualties, South Korea documenting 43 fatalities, and Burma recording 31 lives lost, among others.Moreover, there has been significant damage to critical infrastructure, encompassing various forms of infrastructure, such as transportation networks and buildings.To address this challenge, researchers have turned to Geographic Information Systems (GIS) for studying and analyzing landslide risks.However, they face a limitation: the analysis results mainly consist of static maps, lacking a real-time digital platform for efficient data management and dynamic display of landslide information.This limitation hampers effective monitoring and surveillance efforts.To overcome this limitation, the present study introduces an innovative approach that integrates GIS with the development of a digital platform, effectively addressing the existing challenges.This integrated solution not only enables accurate landslide risk analysis with an impressive accuracy rate of 83.50% but also provides users with the ability to access information about specific landslide sites within a defined radius, regardless of their geographical location.Unlike previous studies, this research demonstrates the potential to address past methodological limitations by utilizing a real-time data platform.Accuracy varies based on geographic context, and landslide occurrences are influenced by diverse factors in different terrains.The digital platform elucidated in this study holds applicability for utilization by disaster prevention and relief organizations, as well as government agencies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".