Short-term hydro-meteorological forecasting with extreme learning machines
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
In machine learning (ML), the extreme learning machine (ELM), a feedforward neural network model which assigns random weights in the single hidden layer and optimizes only the weights in the output layer, has the fully nonlinear modelling capability of the traditional artificial neural network (ANN) model but is solved via linear least squares, as in multiple linear regression (MLR). Chapter 2 evaluated ELM against MLR and three nonlinear ML methods (ANN, support vector regression and random forest) on nine environmental regression problems. ELM was then developed for short-term forecasting of hydro-meteorological variables. In situations where new data arrive continually, the need to make frequent model updates often renders ANN impractical. An online learning algorithm – the online sequential extreme learning machine (OSELM) – is automatically updated inexpensively as new data arrive. In Chapter 3, OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1–3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates, with the nonlinear OSELM easily outperforming the benchmark, the online sequential MLR (OSMLR). A major limitation of OSELM is that the number of hidden nodes (HN), which controls the model complexity, remains the same as in the initial model, even when the arrival of new data renders the fixed number of HN sub-optimal. A new variable complexity online sequential extreme learning machine (VC-OSELM), proposed in Chapter 4, automatically adds or removes HN as online learning proceeds, so the model complexity self-adapts to the new data. For streamflow predictions at lead time of one day, VC-OSELM outperformed OSELM when the initial number of HN turned out to be smaller or larger than optimal. In summary, by using linear least squares instead of nonlinear optimization, ELM offers a major advantage over a traditional method like ANN. In situations where new data arrive continually, OSELM and VC-OSELM were shown in this thesis to be more useful than ANN and OSMLR.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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