Prediction of three vital rainfall characteristics: Advanced hybrid tree- or lazy-based learner?
Bibliographic record
Abstract
Rainfall is considered the most important factor affecting floods, soil erosion, water quality, and groundwater resources. Therefore, accurate rainfall prediction is imperative for the efficient management of land and water resources. This study was aimed at investigating the capabilities of ensemble machine learning algorithms in predicting rainfall characteristics, including the monthly rainfall volume, maximum daily rainfall, and number of rainy days. Specifically, tree-based (random forest and dual perturb and combine tree), and lazy learner (Kstar and instance-based K-nearest neighbors) models, along with their hybridized with rotation forest model (ROF) were used for the rainfall predictions. To meet the aim, nine rainfall-related weather variables, including minimum, maximum, and mean relative humidity; solar radiation; minimum, maximum, and mean temperature; evaporation; and wind speed were considered as inputs in Fars province, Iran. All models were evaluated using qualitative and standard statistical metrics. Minimum relative humidity was found to be the most significant/sensitive input variable in all cases (correlation coefficient between 0.77 and 0.85), while wind speed was found to be the least effective variable (correlation coefficient between 0.15 and 0.20). Minimum relative humidity data alone were sufficient to build efficient input scenarios for all models for the given region and timeframe. Based on the performance metrics, the ROF-Kstar hybrid model was found to be the most effective and robust for the prediction of all the response variables with Nash Sutcliff Efficiency ranges between 0.65 and 0.70. Overall, lazy-based learner models outperformed the tree-based models. This study highlights the potential of proposed novel techniques to predict rainfall characteristics, which can be extended to other domains.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".