Novel method for flood-affected area prediction based on non-equigap multivariable grey model
Bibliographic record
Abstract
Purpose As global climate change intensifies, flood disasters occur frequently, causing severe impacts on agriculture and the socioeconomic environment. Accurate prediction of the affected area of flood disasters is crucial. Considering that historical flood disaster years exhibit characteristics such as non-equidistant intervals, multivariable influences and strong nonlinearity – while existing studies mainly focus on improvements to equidistant grey multivariable models or non-equigap GM (1,1) and MGM (1, m) models – this study proposes a combined forecasting approach integrating the grey disaster model with a non-equigap multivariable grey prediction model (NE-GM (1, N)). Design/methodology/approach First, the grey disaster model is used to predict future disaster years, determining the specific time points for potential flood disasters. Then, by introducing factors such as precipitation and population density, the NE-GM (1, N) model is applied to predict the affected area of flood disasters for those years. This model integrates the principle of giving priority to new information and polynomial expansion techniques, enhancing the response capability to real-time changes and improving prediction accuracy by capturing nonlinear relationships. Findings Verification with data from Hunan and Hubei Provinces shows that this model outperforms traditional methods in terms of prediction accuracy and stability. Originality/value Providing more accurate information for disaster early warning and emergency management, while laying the foundation for further research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".