A Combined Statistical and Machine Learning Approach for Predicting Surface Water Quality in Burkina Faso
Bibliographic record
Abstract
Surface water in Burkina Faso is essential for domestic use, agriculture, and ecosystem services, yet it is increasingly impacted by human activities and climate variability. This study used the Water Quality Index (WQI), multivariate statistics and a Multilayer Perceptron (MLP) neural network to assess and predict water quality. A total of 139 samples were analyzed for 17 physicochemical parameters. The results revealed slightly alkaline waters ([pH] 6.04–9.23), low-to-moderate mineralization (electric conductivity [EC] 39 – 387 micro siemens per centimeter [µS/cm]; total dissolved solids [TDS] 39 –1100 milligrams per liter [mg/L]), and spatially variable nutrient concentrations (ammonium [NH₄⁺], nitrate [NO₃⁻], and phosphate [PO₄³⁻]), which are indicative of both natural and anthropogenic inputs. Correlation and factor analyses identified three main influences on water quality: geogenic weathering; nutrient and sediment inputs from human activities; and salinity and mineral contributions. MLP modelling showed that deeper architectures with two hidden layers (12, 6 and 12,12) achieved the highest predictive accuracy (R² ≈ 0.825, RMSE ≈ 61, and MAE ≈ 40), and the best model generalized well to test data (R²_(Test) = 0.95, RMSE_(Test) = 37.3). This integrated approach shows the potential for combining statistical analysis and machine learning to monitor, manage, and predict surface water quality in Burkina Faso.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".