---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- conflict-violence
- food-security
- cmr
- cod
- irq
- lbn
- mli
pretty_name: "Conflict-Related Incidents Affecting Water Systems"
dataset_info:
  splits:
    - name: train
      num_examples: 340
    - name: test
      num_examples: 85
---

# Conflict-Related Incidents Affecting Water Systems

**Publisher:** Insecurity Insight · **Source:** [HDX](https://data.humdata.org/dataset/conflict-related-incidents-affecting-water-systems) · **License:** `cc-by-igo` · **Updated:** 2026-05-04

---

## Abstract

This page contains data on conflict events with clearly foreseeable impacts on or links to water systems based on agency-and open source events. Categorized by country. Covers Cameroon, DRC, Lebanon, Mali, Myanmar, Niger, oPt, Somalia, Sudan, Syria and Yemen. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

Each row in this dataset represents discrete events or incidents. Temporal coverage is indicated by the `date`, `date_event_entered` column(s). Geographic scope: **CMR, COD, IRQ, LBN, MLI, NER, SSD, PSE, and 4 others**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Discrete events or incidents |
| **Rows (total)** | 426 |
| **Columns** | 15 (2 numeric, 10 categorical, 3 datetime) |
| **Train split** | 340 rows |
| **Test split** | 85 rows |
| **Geographic scope** | CMR, COD, IRQ, LBN, MLI, NER, SSD, PSE, and 4 others |
| **Publisher** | Insecurity Insight |
| **HDX last updated** | 2026-05-04 |

---

## Variables

**Geographic** — `country` (OPT, Syria, Ukraine), `country_iso` (PSE, SYR, UKR), `admin_1` (West Bank, Gaza Strip, South Governorate), `water_infrastructure_category_affected` (Water Distribution, Multi Purpose/Function Infrastructure, Water Storage), `number_of_attacks_damaging_destroying_water_infrastructure` (range 1.0–7.0).

**Temporal** — `date`, `date_event_entered`, `date_event_modified`.

**Identifier / Metadata** — `reported_perpetrator_name` (Israeli Defence Forces, Armed Forces of the Russian Federation, Unidentified Armed Actor), `sind_event_id` (range 4537.0–126962.0), `esa_source` (HDX), `esa_processed` (2026-05-04).

**Other** — `geo_precision` (censored), `reported_perpetrator` (Government: Military, Foreign Forces: Military, NSA), `weapon_carried_used` (Firearms, Aerial Bomb: Plane, Aerial Bomb: Drone).

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-food-security-all")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `date` | datetime64[ns] | 0.0% |  |
| `country` | object | 0.0% | OPT, Syria, Ukraine |
| `country_iso` | object | 0.0% | PSE, SYR, UKR |
| `admin_1` | object | 0.0% | West Bank, Gaza Strip, South Governorate |
| `geo_precision` | object | 0.0% | censored |
| `reported_perpetrator` | object | 0.0% | Government: Military, Foreign Forces: Military, NSA |
| `reported_perpetrator_name` | object | 0.0% | Israeli Defence Forces, Armed Forces of the Russian Federation, Unidentified Armed Actor |
| `weapon_carried_used` | object | 0.0% | Firearms, Aerial Bomb: Plane, Aerial Bomb: Drone |
| `water_infrastructure_category_affected` | object | 8.9% | Water Distribution, Multi Purpose/Function Infrastructure, Water Storage |
| `number_of_attacks_damaging_destroying_water_infrastructure` | float64 | 36.4% | 1.0 – 7.0 (mean 1.0554) |
| `sind_event_id` | int64 | 0.0% | 4537.0 – 126962.0 (mean 105978.0587) |
| `date_event_entered` | datetime64[ns] | 0.0% |  |
| `date_event_modified` | datetime64[ns] | 0.0% |  |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-05-04 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `number_of_attacks_damaging_destroying_water_infrastructure` | 1.0 | 7.0 | 1.0554 | 1.0 |
| `sind_event_id` | 4537.0 | 126962.0 | 105978.0587 | 100152.5 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 13 column(s) with >80% missing values were removed: `event_description`, `latitude`, `longitude`, `number_of_attacks_where_water_infrastructure_was_contaminated`, `number_of_attacks_where_water_infrastructure_was_looted`, `number_of_attacks_where_water_infrastructure_was_obstructed`.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from Insecurity Insight and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `number_of_attacks_damaging_destroying_water_infrastructure`.
- This dataset spans 12 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/conflict-related-incidents-affecting-water-systems) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_asia_food_security_all,
  title     = {Conflict-Related Incidents Affecting Water Systems},
  author    = {Insecurity Insight},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/conflict-related-incidents-affecting-water-systems},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*