Applying Upstream Satellite Signals and a 2-D Error Minimization Algorithm to Advance Early Warning and Management of Flood Water Levels and River Discharge
Bibliographic record
Abstract
Recent studies demonstrate the power of applying satellite imagery in combination with artificial intelligence (AI) methods to advance the accuracy of forecasting ungauged river network water levels and discharge for early flood warning and management. In predicting river water levels and discharge time series, one of the most common sources of error with AI forecasting algorithms is the input imitation defect. When the input imitation defect occurs, regression methods simply present the input variables as output. In this paper, the input imitation defect is minimized by first introducing the two concepts of vertical error and horizontal error. Subsequently, upstream imagery information is combined with previous lags to propose a new procedure for predicting future satellite signals accurately and with the lowest possible input imitation defect. To accomplish this, the brightness temperature received by the Advanced Microwave Scanning Radiometer is used as a proxy of river discharge. The proposed method (PM) is finally compared with the simple linear regression and three well-known AI methods, i.e., multilayer perceptron, extreme learning machines, and radial basis function. The study outcome indicates that the PM results are more trustworthy and realistic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".