Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins
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
Radar-based rainfall forecasts are widely used extrapolation algorithms that are popular in systems of precipitation for predicting up to six hours in lead time. Nevertheless, the reliability of rainfall forecasts gradually declines for heavy rain events with lead time due to the lack of predictability. Recently, data-driven approaches were commonly implemented in hydrological problems. In this research, the data-driven models were developed based on the data obtained from a radar forecasting system named McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE) and ground rain gauges. The data included thirteen urban stations in the five metropolitan cities located in South Korea. The twenty-five data points of MAPLE surrounding each rain station were utilized as the model input, and the observed rainfall at the corresponding gauges were used as the model output. The results showed superior capabilities of long short-term memory (LSTM) network in improving 180-min rainfall forecasts at the stations based on a comparison of five different data-driven models, including multiple linear regression (MLR), multivariate adaptive regression splines (MARS), multi-layer perceptron (MLP), basic recurrent neural network (RNN), and LSTM. Although the model still produced an underestimation of extreme rainfall values at some examined stations, this study proved that the LSTM could provide reliable performance. This model can be an optional method for improving rainfall forecasts at the stations for urban basins.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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