Artificial intelligence-assisted prediction of critical factors governing pesticide adsorption by agricultural wastes in wastewater treatment
Bibliographic record
Abstract
Agricultural waste-based adsorbents offer a sustainable, cost-effective solution for pesticide removal via adsorption. However, predicting removal efficiency under diverse experimental conditions remains challenging, especially when combining quantitative and qualitative variables. This study proposes a robust machine learning framework to predict pesticide removal efficiency using Random Forest (RF), Least-Square Boosting (LSBoost), and Extreme Learning Machine (ELM), each optimized through Bayesian Optimization with six acquisition functions. A curated dataset of 1,983 samples from the literature was used, comprising eight input variables: pesticide type, adsorbent type, initial concentration, contact time, pH, adsorbent to solvent ratio, pretreatment type, and adsorbent form. The target variable was pesticide removal efficiency (%). To determine optimal input combinations, 255 RF-based models (1–8 variables) were developed using cross-validation. RF consistently outperformed other models, achieving the highest predictive accuracy (R = 0.970, NSE = 0.937) and lowest error (NRMSE = 0.099, MAPE = 13.19%). LSBoost produced moderate accuracy, particularly with fewer variables, but showed sensitivity to input structure. ELM underperformed, especially with heterogeneous input features. Feature importance analysis revealed contact time, initial concentration, and adsorbent-to-solvent ratio as the most influential variables, whereas pretreatment type and adsorbent form had minimal impact. Pesticide-specific RF models showed that contact time, adsorbent-to-solvent ratio, and initial concentration were key for atrazine and chlorpyrifos removal, while adsorbent type was also critical for carbofuran. For diazinon, contact time, adsorbent type, and adsorbent-to-solvent ratio were the primary predictors. The findings of this study offer valuable guidance for designing efficient, sustainable pesticide removal systems using agro-waste-based adsorbents.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".