Multi-step-ahead soil temperature forecasting at multiple-depth based on meteorological data: Integrating resampling algorithms and machine learning models
Bibliographic record
Abstract
Abstract Direct soil temperature (ST) measurement is time-consuming and costly; thus, the use of a simple and cost-effective machine learning (ML) tool is helpful. In this study, ML approaches, including KStar, instance-based K-nearest learner (IBK) and locally weighted learner (LWL) coupled with resampling algorithms of bagging (BA) and dagging (DA) were developed and tested for multi-step ahead (3, 6 and 9 days ahead) ST forecasting. In addition, a linear regression model (LR) was used as a benchmark to compare the results. A dataset with daily ST time-series (as models’ output) along with meteorological data (mean (TMean), minimum (TMin) and maximum (TMax) air temperature, evaporation (Eva), sunshine hours (SSH) and solar radiation (SR); as models’ input) were collected at Isfahan synoptic station (Iran), in a farmland, during 13 years (1992–2005) at 5 and 50 cm soil depths. Six different input combination scenarios were proposed to the models based on Pearson’s correlation coefficients between inputs and outputs. For the model building, we used 70% of the data and the remaining 30% was considered for model evaluation through different visual and quantitative metrics. Our findings showed that variable TMean is the most effective input variable for ST forecasting in most of the developed algorithms, while in some cases the combination of several variables including TMean, TMax and as well as the integration of TMean, TMax, TMin, Eva and SSH proved to be the best input combinations. Among the evaluated models, KStar showed more compatibility with the BA algorithm, while, in most cases and depending on soil depth, IBK and LWL obtained more accurate results when they were hybridized with DA. For soil depth of 5 cm, BA-KStar has superior performance (i.e. Nash-Sutcliffe Efficiency (NSE) = 0.90, 0.87 and 0.85 for 3, 6 and 9 months ahead forecasting, respectively) while for soil depth of 50 cm, DA-KStar outperforms other algorithms (i.e. NSE = 0.88, 0.89 and 0.89 for 3, 6 and 9 months ahead forecasting, respectively). Also, results confirmed that all hybrid models had higher prediction capability than the LR model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".