Monitoring volcanic gas hazards in Goma DRC using GIS and Google Earth Engine
Bibliographic record
Abstract
Abstract Nyiragongo volcano, situated dangerously close to the densely populated city of Goma, poses a constant and immediate threat. This research investigated the 2021 Nyiragongo eruption, employing a multi-faceted approach to characterize lava flow trajectories and the emission of deleterious gases (CO₂, CO, and NO₂). Methodologically, the study integrated field observations, satellite remote sensing via Google Earth Engine (GEE), and Geographic Information Systems (GIS) to delineate lava flow extents. Furthermore, in situ analyses documented structural damage to roadways in Goma and adjacent areas, attributed to seismic wave propagation. Despite the eruption’s relatively limited magnitude on May 20, 2021, field assessments revealed substantial localized devastation, particularly resulting from lava inundation of built infrastructure. The integration of GIS mapping into urban planning strategies is paramount for reconstruction initiatives in Goma and analogous regions. These geospatial analyses can delineate hazardous zones based on established lava flow patterns, thereby mitigating future infrastructural development in high-risk areas. While ground instruments are limited in their capacity to determine plume density and dispersal, critical parameters for eruption forecasting. Conversely, satellite remote sensing offers a synoptic perspective, enabling the monitoring of gas emission dynamics across extensive spatial domains. This study leveraged satellite imagery to analyze the spatiotemporal evolution of volcanic gas halos associated with Nyiragongo volcano during pre-eruptive, eruptive, and post-eruptive phases of two distinct events. Comparative analysis of sequential satellite observations facilitated the identification of recurring patterns in gas density and distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".