Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data
Bibliographic record
Abstract
Himalayan watersheds are characterized by mountainous topography and a lack of available data. Due to the complexity of rainfall–runoff relationships in mountainous watersheds and the lack of hydrological data in many of these watersheds, process-based models have limited applicability for runoff forecasting in these areas. In light of this, accurate forecasting methods that do not necessitate extensive data sets are required for runoff forecasting in mountainous watersheds. In this study, multivariate adaptive regression spline (MARS), wavelet transform artificial neural network (WA-ANN), and regular artificial neural network (ANN) models were developed and compared for runoff forecasting applications in the mountainous watershed of Sainji in the Himalayas, an area with limited data for runoff forecasting. To develop and test the models, three micro-watersheds were gauged in the Sainji watershed in Uttaranchal State in India and data were recorded from July 1 2001 to June 30 2003. It was determined that the best WA-ANN and MARS models were comparable in terms of forecasting accuracy, with both providing very accurate runoff forecasts compared to the best ANN model. The results indicate that the WA-ANN and MARS methods are promising new methods of short-term runoff forecasting in mountainous watersheds with limited data, and warrant additional study.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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 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".