"""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()