Evaluating the Neural Network Ensemble Method in Predicting Soil Moisture in Agricultural Fields
Bibliographic record
Abstract
Soil is an important element in the agricultural domain because it serves as the media that bridges the water consumption and supply processes. In this study, a neural network ensemble (NNE) method was employed to predict the soil moisture to eliminate the effects of random initial parameters of neural network (NN) on model accuracy. The constructed NNE model predicts daily root zone soil moisture continuously for the whole crop growing season and the water consumption and supply processes were separately modeled. The soil profile was divided into multiple layers and modeled separately. Weather data (including air temperature, humidity, wind speed, net radiation, and precipitation), rooting depth, and the hesternal soil moisture of each layer were used as the input. A calibrated root zone water quality model for maize (Zea mays L.) was used to generate training and evaluation data. The result showed that with 100 randomly initialized NN models, the NNE model achieved an average R2 of 0.96 and nRMSE of 5.93%, suggesting that the NNE model learned the soil moisture dynamics well and sufficiently improved the robustness of soil moisture prediction with high accuracy.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".