Long-Term Forecasting of Crop Water Requirement with BP-RVM Algorithm for Food Security and Harvest Risk Reduction
Bibliographic record
Abstract
Cropping pattern planning is important to avoid crop failure.Meanwhile, cropping patterns are affected by climate change, which is constantly shifting and erratic.Mistakes in determining the planting schedule will affect the risk of crop failure.Hence, climate forecast using long-term hydro-climatological data must be conducted as cropping patterns are mapped for a multi-year period.Data was collected from the Meteorology, Climatology, and Geophysics Agency in Lombok Island.This paper discusses the combination of backpropagation and relevance vector machine with RBF kernel.We utilized BP-RVM architecture with three hidden layers to improve the performance of the network.This combination is utilized because of the BP algorithm's ability to simplify data pattern recognition and RVM to speed up and reduce the number of iterations for each data training-testing process.The evapotranspiration of each crop was then calculated using the FAO24 Blaney-Criddle method.Based on the forecasting, the average MAPE was below 20%, which indicates "good forecasting".The evapotranspiration values of CGPRT and horticultural crops were almost the same with an average of 2.79 mm/day and 2.78 mm/day.These values are lower than the evapotranspiration values of tobacco and rice.Finally, based on the calculation of each crop's water requirement throughout the year, it was recommended to start the first planting season at the end of October.The results of this study can be recommended to the government to apply the BP-RVM algorithm in forecasting hydro-climatological data and optimizing cropping patterns to avoid crop failure and maintain the stability of national food security.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".