Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water
Bibliographic record
Abstract
This study explored the application of machine learning, specifically artificial neural network (ANN), to create prediction models for manganese (Mn) concentration in soil and surface water (SW) on the island province with two open mine pits overflowing to two major rivers that experienced mining disasters. The two ANN models were created to predict Mn concentrations in soil and SW from 12 and 14 input parameters for soil and SW, respectively. These input parameters were extracted from extensive field data collected at the site during sampling program in 2019, 2021, 2022, and initially processed with spatial analysis via geographic information system (GIS). All datasets were then divided for model training and validation, using 85% and 15% ratio, respectively. Performance evaluation of each model with mean absolute percentage error (MAPE) and root mean squared error (RMSE) confirmed the accuracy of both models. The soil Mn model achieved MAPE and RMSE values of 2.01% and 23.98, respectively. The SW Mn model was split into two models based on SW Mn values within the 0–1 mg/L range and >1 mg/L range. The SW Mn model for >1 mg/L performed better with MAPE and RMSE of 4.61% and 0.17, respectively. Feature reduction was also conducted to identify how the models will perform if some input parameters were excluded. Result showed sufficient accuracy can still be obtained with the removal of 4–5 input parameters. This study and these models highlight the benefit of ANN to the scientific community and government units, for predicting Mn concentration, of similar environmental conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".