River flood prediction using fuzzy neural networks: an investigation on automated network architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban floods are one of the most devastating natural disasters globally and improved flood prediction is essential for better flood management. Today, high-resolution real-time datasets for flood-related variables are widely available. These data can be used to create data-driven models for improved real-time flood prediction. However, data-driven models have uncertainty stemming from a number of issues: the selection of input data, the optimisation of model architecture, estimation of model parameters, and model output. Addressing these sources of uncertainty will improve flood prediction. In this research, a fuzzy neural network is proposed to predict peak flow in an urban river. The network uses fuzzy numbers to account for the uncertainty in the output and model parameters. An algorithm that uses possibility theory is used to train the network. An adaptation of the automated neural pathway strength feature selection (ANPSFS) method is used to select the input features. A search and optimisation algorithm is used to select the network architecture. Data for the Bow River in Calgary, Canada are used to train and test the network.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it