Design of a hybrid intelligent system for the management of flood disaster risks
Bibliographic record
Abstract
The frequency of occurrence and intensity of floods is a huge threat to environment, human existence, critical infrastructure and economy. Flood risk assessments depend on probabilistic approaches and suffer from non-existence of appropriate indices of acceptable risk, dearth of information and pieces of knowledge for explicit view and understanding of the characteristics and severity level of flood hazard. This paper proposes a hybridized intelligent framework comprising fuzzy logic (FL), neural network and genetic algorithm for clustering and visualization of flood data, prediction and classification of flood risks severity level. A multidimensional knowledge model of flood incidence using star, snowflake and facts constellation schemas was proposed for the knowledge warehouse. A six-layered adaptive neuro-fuzzy inference system implementing mamdani’s inference mechanism was design to evaluate input features based on fuzzy rules held in the multidimensional data model. The system is aimed at predicticting and classifying flood risk severity levels. The perception of emergency risk management is very important in modern society. Therefore, this work provides a framework for the practical applications of data mining techniques and tools to emergency risk management. The work would assist to identify locations with significant flood risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".