---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - mining
  - tailings-dam
  - dam-safety
  - risk-assessment
  - environmental
  - geotechnical
  - africa
  - synthetic-data
pretty_name: African Tailings Dam Risk Dataset
size_categories:
  - 1K<n<10K
---

# African Tailings Dam Risk Dataset

## Dataset Description

### Overview

This dataset provides synthetic monitoring records and risk assessments for tailings storage facilities (TSFs) across African mining operations. Tailings dams represent one of the most significant environmental and safety risks in mining, with catastrophic failures causing loss of life and long-term environmental damage.

The dataset captures dam characteristics, geotechnical monitoring data, environmental conditions, and risk indicators aligned with the Global Industry Standard on Tailings Management (GISTM).

### Dataset Statistics

| Attribute | Value |
|-----------|-------|
| Records | 5,000 |
| Variables | 31 |
| Temporal Coverage | 2020-2024 |
| Geographic Scope | 8 African countries |
| Facility Count | ~500 unique TSFs |
| Format | CSV, Parquet |

## Data Schema

### Variable Dictionary

| Variable | Type | Description | Value Range/Categories |
|----------|------|-------------|----------------------|
| `record_id` | string | Unique monitoring record ID | TSF-XXXXXXXX |
| `facility_id` | string | Tailings facility identifier | FAC-XXXX |
| `country` | categorical | Country of facility | south_africa, ghana, drc, zambia, zimbabwe, tanzania, mali, mauritania |
| `commodity` | categorical | Primary commodity | gold, copper, platinum, iron_ore, coal, diamond, uranium, other |
| `operational_status` | categorical | Facility status | active, inactive, closed, under_construction |
| `dam_type` | categorical | Dam construction type | earthfill, rockfill, concrete, composite |
| `construction_method` | categorical | Raise method (critical for stability) | upstream, downstream, centerline, dry_stack |
| `max_height_m` | float | Maximum design height | 10-250 meters |
| `current_height_m` | float | Current embankment height | meters |
| `storage_capacity_mm3` | float | Total storage capacity | million m³ |
| `current_volume_mm3` | float | Current stored volume | million m³ |
| `catchment_area_km2` | float | Contributing catchment area | km² |
| `year_constructed` | integer | Initial construction year | 1960-2023 |
| `design_life_years` | integer | Designed operational life | 20-100 years |
| `last_raise_year` | integer | Year of most recent raise | Year |
| `consequence_classification` | categorical | Failure consequence class | extreme, very_high, high, significant, low |
| `monitoring_date` | datetime | Date of monitoring record | 2020-01-01 to 2024-12-31 |
| `freeboard_m` | float | Water surface to crest distance | 0.5-10 meters |
| `piezometer_level_m` | float | Phreatic surface height | meters |
| `seepage_rate_lpm` | float | Measured seepage rate | liters per minute |
| `settlement_mm` | float | Cumulative crest settlement | millimeters |
| `inclinometer_displacement_mm` | float | Lateral displacement | millimeters |
| `rainfall_24h_mm` | float | 24-hour precipitation | millimeters |
| `earthquake_pga` | float | Peak ground acceleration | g (gravitational) |
| `water_balance_status` | categorical | Water management status | surplus, balanced, deficit |
| `factor_of_safety` | float | Calculated stability FoS | 0.8-2.5 |
| `risk_score` | float | Composite risk score | 0-100 |
| `risk_category` | categorical | Risk classification | low, medium, high, critical |
| `last_inspection_days_ago` | integer | Days since last inspection | 1-730 days |
| `governance_score` | integer | Management system score | 20-100 |
| `has_eap` | boolean | Emergency Action Plan exists | True/False |

## Methodology

### Tailings Dam Risk Framework

The dataset models risk factors identified in major tailings dam failures and GISTM requirements:

**Construction Method Risk Ranking**:
| Method | Risk Level | Prevalence | Notes |
|--------|------------|------------|-------|
| Upstream | Highest | 35% | Sequential raises on tailings beach; liquefaction vulnerable |
| Centerline | Medium | 25% | Raises vertically; moderate stability |
| Downstream | Lower | 30% | Raises downstream; best stability |
| Dry Stack | Lowest | 10% | Filtered tailings; no impoundment |

**Key Risk Indicators Modeled**:

1. **Factor of Safety (FoS)**: Ratio of resisting to driving forces
   - Critical threshold: FoS < 1.3
   - Upstream dams: Mean FoS reduced by 0.2
   - Dry stack: Mean FoS increased by 0.3

2. **Freeboard**: Buffer against overtopping
   - Critical threshold: < 1.5 meters

3. **Seepage**: Indicator of internal erosion
   - Warning threshold: > 100 LPM

4. **Displacement**: Slope movement indicator
   - Warning threshold: > 30 mm cumulative

### Historical Context

Major African tailings incidents informing risk modeling:
- Merriespruit (South Africa, 1994): 17 fatalities, upstream dam
- Samarco analogs: Liquefaction failure modes
- Regional seismicity considerations

### Sensor Correlation Structure

Monitoring parameters are correlated to reflect realistic failure precursors:
- Rainfall → Piezometer rise → Seepage increase → Settlement/displacement
- Seismic events → Pore pressure spike → Stability reduction

## Limitations

1. **Simplified Geotechnics**: Complex soil mechanics reduced to statistical distributions
2. **Sensor Density**: Real TSFs have hundreds of instruments; dataset simplified
3. **Failure Events**: Actual failures not explicitly modeled (rare events)
4. **Site Specificity**: Generic African context; site investigations required
5. **Climate Projections**: Historical patterns; climate change effects not modeled
6. **Governance Proxies**: Management quality simplified to single score

## Ethical Considerations

- **Public Safety**: Tailings failures can kill hundreds and devastate communities
- **Environmental Justice**: Downstream communities often marginalized
- **Corporate Accountability**: Dataset should not be used to obscure real risks
- **Regulatory Implications**: Not a substitute for proper engineering assessment
- **Transparency**: Supports calls for public disclosure of TSF risks

## Intended Uses

### Appropriate Uses

- Development of early warning algorithms
- Research on risk indicator correlations
- Educational demonstrations of dam safety monitoring
- Benchmarking ML approaches for anomaly detection
- Policy research on tailings governance

### Inappropriate Uses

- Actual dam safety assessments
- Regulatory compliance certification
- Insurance or liability determinations
- Investment decisions on specific facilities
- Replacing qualified geotechnical engineering

## Citation

```bibtex
@dataset{electric_sheep_africa_tailings_dam_2024,
  title = {African Tailings Dam Risk Dataset},
  author = {Electric Sheep Africa},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/african-mining-tailings-dam},
  note = {Synthetic dataset for tailings dam risk research aligned with GISTM}
}
```

## References

1. ICOLD (International Commission on Large Dams). (2001). *Tailings Dams: Risk of Dangerous Occurrences*. Bulletin 121.

2. Global Tailings Review. (2020). *Global Industry Standard on Tailings Management (GISTM)*. ICMM, UNEP, PRI.

3. Santamarina, J.C., Torres-Cruz, L.A., & Bachus, R.C. (2019). Why coal ash and tailings dam disasters occur. *Science*, 364(6440), 526-528.

4. Bowker, L.N., & Chambers, D.M. (2015). The risk, public liability, & economics of tailings storage facility failures. *Earthwork Act*.

5. Franks, D.M., et al. (2021). Tailings facility disclosures reveal stability risks. *Scientific Reports*, 11, 5353.

## License

This dataset is released under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) (CC-BY-4.0).

## Contact

For questions or feedback, please open an issue on the dataset repository or contact Electric Sheep Africa.