Taşkın Modelleme ve Risk Analizinde LiDAR Verisiyle Sayısal Yükseklik Modeli Üretimi
Bibliographic record
Abstract
Floods are one of the most serious, widespread and costly disasters in the world that too many countries came across every year. As the threat of Global Warming increases nowadays, floods are gradually becoming global threat to the human being. Turkey, having third largest hydroelectric potential in Europe, is greatly exposed to flood origin threats. LiDAR (Light detection and ranging) technology is relatively a new technology for flood modelling and risk analysis, but it is being widely used in some countries, Canada, USA, UK, etc. successfully for a decade. When LiDAR technology is compared with the technics (classical land survey, photogrammetry, etc) previously used, it comes to the forefront with its great advantages. LiDAR technology collects high-accuracy elevation data (better than 30 cm.) for very large areas very quickly and at lower cost than traditional methods. A LiDAR system uses laser beams which pulse tens of thousands of times a second. This results in very high point density and so, high accuracy in model building which is the most important data for hydrologic and flood modelling. The aim of this study is to implement LiDAR image processing procedures in a little basin comprehensively with the help of the ortho-photos acquired from the digital aerial photos taken concurrently and draw attention to the advantages of LiDAR technology especially on acquiring high accuracy DEM and its availability for Hydrologic Modelling and flood risk analysis procedures to be conducted with Geographic Information System (GIS), afterwards. In this study, an airborne LiDAR data of Borçka district of Artvin city (consisted of 370.000.000 laser points) and the aerial photos taken concurrently were used. Terrasolid LiDAR softwares and Bentley's Microstation V8i CAD softwares were used for processing the raw data and creating Digital Elevation Model (DEM). ArcGIS 10.1 was used for Hydrologic Modelling. This study will be basis of a more comprehensive doctoral thesis, which will include flood risk analysis in several basins together with LIDAR data and multispectral satellite images, comparing the advantages and disadvantages of both technologies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".