| """Generate synthetic sea-level rise & coastal health infrastructure dataset for SSA.""" |
|
|
| from __future__ import annotations |
|
|
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| SEED = 42 |
| N_PER_SCENARIO = 10_000 |
|
|
| YEAR_RANGE = np.arange(2010, 2025) |
| YEAR_WEIGHTS = np.linspace(0.85, 1.3, len(YEAR_RANGE)) |
| YEAR_WEIGHTS = YEAR_WEIGHTS / YEAR_WEIGHTS.sum() |
|
|
| SCENARIOS = { |
| "west_africa_delta": { |
| "slr_mm_per_yr_mean": 3.5, |
| "slr_mm_per_yr_sd": 0.8, |
| "storm_surge_freq": 0.25, |
| "coastal_erosion_m_per_yr": 2.5, |
| "elevation_mean": 3.0, |
| "elevation_sd": 2.0, |
| "flood_risk_base": 0.35, |
| "saltwater_intrusion_pct": 0.40, |
| "facility_at_risk_pct": 0.30, |
| "facility_damaged_pct": 0.12, |
| "population_density_mean": 450, |
| "displacement_pct": 0.20, |
| "waterborne_disease_rate": 120, |
| "malaria_rate_per1k": 180, |
| "cholera_risk": 0.15, |
| "mental_health_impact": 0.25, |
| "setting_probs": {"coastal_urban": 0.35, "coastal_rural": 0.40, "delta": 0.25}, |
| }, |
| "east_africa_island": { |
| "slr_mm_per_yr_mean": 4.0, |
| "slr_mm_per_yr_sd": 1.0, |
| "storm_surge_freq": 0.30, |
| "coastal_erosion_m_per_yr": 1.8, |
| "elevation_mean": 4.5, |
| "elevation_sd": 2.5, |
| "flood_risk_base": 0.28, |
| "saltwater_intrusion_pct": 0.35, |
| "facility_at_risk_pct": 0.25, |
| "facility_damaged_pct": 0.10, |
| "population_density_mean": 300, |
| "displacement_pct": 0.15, |
| "waterborne_disease_rate": 95, |
| "malaria_rate_per1k": 140, |
| "cholera_risk": 0.10, |
| "mental_health_impact": 0.22, |
| "setting_probs": {"coastal_urban": 0.30, "coastal_rural": 0.35, "island": 0.35}, |
| }, |
| "southern_coastal_city": { |
| "slr_mm_per_yr_mean": 3.0, |
| "slr_mm_per_yr_sd": 0.6, |
| "storm_surge_freq": 0.20, |
| "coastal_erosion_m_per_yr": 1.5, |
| "elevation_mean": 6.0, |
| "elevation_sd": 3.0, |
| "flood_risk_base": 0.22, |
| "saltwater_intrusion_pct": 0.25, |
| "facility_at_risk_pct": 0.18, |
| "facility_damaged_pct": 0.07, |
| "population_density_mean": 600, |
| "displacement_pct": 0.10, |
| "waterborne_disease_rate": 65, |
| "malaria_rate_per1k": 80, |
| "cholera_risk": 0.06, |
| "mental_health_impact": 0.18, |
| "setting_probs": {"coastal_urban": 0.55, "coastal_rural": 0.25, "peri_urban": 0.20}, |
| }, |
| } |
|
|
| SCENARIO_FILES = { |
| "west_africa_delta": "slr_west_africa_delta.csv", |
| "east_africa_island": "slr_east_africa_island.csv", |
| "southern_coastal_city": "slr_southern_coastal_city.csv", |
| } |
|
|
|
|
| def _choice(rng, prob_map): |
| keys = list(prob_map.keys()) |
| weights = np.array(list(prob_map.values()), dtype=float) |
| weights = weights / weights.sum() |
| return rng.choice(keys, p=weights) |
|
|
|
|
| def _simulate_scenario(name, params, seed): |
| rng = np.random.default_rng(seed) |
| records = [] |
|
|
| for idx in range(N_PER_SCENARIO): |
| year = int(rng.choice(YEAR_RANGE, p=YEAR_WEIGHTS)) |
| setting = _choice(rng, params["setting_probs"]) |
|
|
| elevation_m = float(np.clip(rng.normal(params["elevation_mean"], params["elevation_sd"]), 0.2, 20)) |
| slr_rate = float(np.clip(rng.normal(params["slr_mm_per_yr_mean"], params["slr_mm_per_yr_sd"]), 1, 8)) |
| cumulative_slr_cm = float(slr_rate * (year - 2000) / 10) |
|
|
| storm_surge = int(rng.random() < params["storm_surge_freq"]) |
| surge_height_m = float(np.clip(rng.exponential(0.8), 0, 3.5)) if storm_surge else 0.0 |
| coastal_erosion = float(np.clip( |
| rng.normal(params["coastal_erosion_m_per_yr"], 0.8), 0, 8 |
| )) |
|
|
| flood_risk = float(np.clip( |
| params["flood_risk_base"] + (cumulative_slr_cm / 15) * 0.2 - (elevation_m / 10) * 0.15, |
| 0, 1, |
| )) |
| flooded = int(rng.random() < flood_risk) |
| saltwater_intrusion = int(rng.random() < params["saltwater_intrusion_pct"] + cumulative_slr_cm * 0.01) |
|
|
| facility_at_risk = int(rng.random() < params["facility_at_risk_pct"]) |
| facility_damaged = int(facility_at_risk and rng.random() < params["facility_damaged_pct"] / params["facility_at_risk_pct"]) |
| facility_inaccessible = int((flooded or facility_damaged) and rng.random() < 0.4) |
|
|
| supply_chain_disrupted = int((flooded or storm_surge) and rng.random() < 0.35) |
| essential_medicine_stockout = int(supply_chain_disrupted and rng.random() < 0.45) |
|
|
| displaced = int(rng.random() < params["displacement_pct"] * (1 + flooded * 0.5)) |
| population_exposed = int(np.clip( |
| rng.normal(params["population_density_mean"], 120) * (1 if elevation_m < 5 else 0.5), |
| 50, 2000, |
| )) |
|
|
| wb_disease_rate = params["waterborne_disease_rate"] * (1 + flooded * 0.6 + saltwater_intrusion * 0.3) |
| waterborne_disease = int(rng.random() < wb_disease_rate / 1000) |
|
|
| malaria_rate = params["malaria_rate_per1k"] * (1 + flooded * 0.4) |
| malaria_case = int(rng.random() < malaria_rate / 1000) |
|
|
| cholera_outbreak = int(rng.random() < params["cholera_risk"] * (1 + flooded * 1.5 + saltwater_intrusion * 0.5)) |
|
|
| mental_health_impact = int(rng.random() < params["mental_health_impact"] * (1 + displaced * 0.5)) |
|
|
| health_system_resilience = float(np.clip( |
| 0.5 |
| - facility_damaged * 0.15 |
| - supply_chain_disrupted * 0.1 |
| - facility_inaccessible * 0.1 |
| + (0.1 if "urban" in setting else 0) |
| + rng.normal(0, 0.05), |
| 0, 1, |
| )) |
|
|
| adaptation_score = float(np.clip( |
| rng.normal(0.3, 0.12) + (0.1 if "urban" in setting else 0), |
| 0, 1, |
| )) |
|
|
| record = { |
| "record_id": f"{name[:3].upper()}-{idx:05d}", |
| "scenario": name, |
| "year": year, |
| "setting": setting, |
| "elevation_m": round(elevation_m, 1), |
| "slr_rate_mm_per_yr": round(slr_rate, 1), |
| "cumulative_slr_cm": round(cumulative_slr_cm, 1), |
| "storm_surge": storm_surge, |
| "surge_height_m": round(surge_height_m, 1), |
| "coastal_erosion_m_per_yr": round(coastal_erosion, 1), |
| "flood_risk": round(flood_risk, 2), |
| "flooded": flooded, |
| "saltwater_intrusion": saltwater_intrusion, |
| "facility_at_risk": facility_at_risk, |
| "facility_damaged": facility_damaged, |
| "facility_inaccessible": facility_inaccessible, |
| "supply_chain_disrupted": supply_chain_disrupted, |
| "essential_medicine_stockout": essential_medicine_stockout, |
| "displaced": displaced, |
| "population_exposed": population_exposed, |
| "waterborne_disease": waterborne_disease, |
| "malaria_case": malaria_case, |
| "cholera_outbreak": cholera_outbreak, |
| "mental_health_impact": mental_health_impact, |
| "health_system_resilience": round(health_system_resilience, 2), |
| "adaptation_score": round(adaptation_score, 2), |
| } |
| records.append(record) |
|
|
| return pd.DataFrame(records) |
|
|
|
|
| def main(): |
| output_dir = Path("data") |
| output_dir.mkdir(parents=True, exist_ok=True) |
| for idx, (name, params) in enumerate(SCENARIOS.items()): |
| df = _simulate_scenario(name, params, SEED + idx * 211) |
| df.to_csv(output_dir / SCENARIO_FILES[name], index=False) |
| print(f"Saved {name} -> {SCENARIO_FILES[name]}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
