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How I Built an Agentic Disaster Analytics Pipeline for Nepal’s Emergency Response

Mausam

Ever been on a call where the first 30 minutes are spent asking “Which district is this flood in?” while the river already breached the embankment? I was in that exact spot during the 2023 monsoon floods in Sindhupalchok, scrambling through a mix of Nepali news snippets and a shaky government API. The experience convinced me that emergency responders need an autonomous pipeline that turns chaotic text streams into clean, geocoded, priority‑ranked incidents—no human in the loop until the very end. If you’re building a real‑time analytics layer for Nepal’s disaster management, this tutorial shows you how to stitch together a fault‑tolerant, multi‑agent workflow that does exactly that.

Key Takeaways

  • Use Pydantic models to enforce schema safety on every incoming JSON payload, preventing malformed reports from contaminating downstream agents.
  • Leverage a lightweight function‑calling LLM (or a deterministic lookup) to resolve ambiguous Nepali place names against a GeoJSON of Nepal’s 753 local levels.
  • Combine deterministic heuristics (e.g., earthquake magnitude) with LLM‑driven sentiment analysis to produce a reproducible severity score that can be triaged automatically.
  • Persist each agent step to an append‑only JSON Lines log so that no incident is ever lost, even when external APIs time out.
  • Fallback strategies (alternative feeds, cached admin boundaries) keep the pipeline alive during peak disaster hours.

The Problem

During monsoon floods and sudden earthquakes, Nepal’s disaster management agencies receive dozens of unstructured reports per minute from the DRR Portal, local news RSS, and crowdsourced SMS gateways. These reports are bilingual (English and Devanagari), often missing coordinates, and sometimes contain contradictory location strings. Manual triage takes hours, which is unacceptable when lives are at stake. The goal of this tutorial is to build a self‑healing, agentic pipeline that ingests those feeds, validates them, normalizes locations, scores severity, and outputs a ready‑to‑consume CSV/JSON for the command center.

Data and Sources

Data accessed on 2026-10-20.

Loading the Data

We start by pulling the most recent incident feed. If the government endpoint fails or returns malformed JSON, we fall back to the USGS feed so the script never crashes during a demo.

import requests, json, logging

def fetch_incidents():
    try:
        r = requests.get("http://drrportal.gov.np/api/v1/incident", timeout=5)
        r.raise_for_status()
        return r.json()
    except (requests.RequestException, json.JSONDecodeError) as e:
        logging.warning(f"DRR portal unavailable: {e}. Falling back to USGS.")
        r = requests.get(
            "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_hour.geojson",
            timeout=5,
        )
        r.raise_for_status()
        return r.json()["features"]

Step 1 – Schema Enforcement with Pydantic

Every raw record is wrapped in a Pydantic model. Validation errors are caught early and logged; the offending record is dropped without breaking the whole pipeline.

from pydantic import BaseModel, Field, ValidationError
from typing import Optional, List

class RawIncident(BaseModel):
    id: str = Field(..., alias="event_id")
    title: str
    description: Optional[str]
    time: int
    latitude: Optional[float] = None
    longitude: Optional[float] = None
    magnitude: Optional[float] = None  # only for earthquakes

Step 2 – Adaptive Geo‑Normalizer Agent

The agent first checks for explicit lat/lon. If they exist, we verify the point lies inside a known district polygon using shapely. When coordinates are missing, we call a lightweight “LLM” (simulated here with a dictionary lookup) to extract a place name from the title, then map it to a polygon.

import geopandas as gpd
from shapely.geometry import Point

# Load admin boundaries once
ADMIN_GDF = gpd.read_file(
    "https://raw.githubusercontent.com/datasets/geo-admin1/master/data/nepal.geojson"
)

def point_in_district(lat, lon):
    pt = Point(lon, lat)
    for _, row in ADMIN_GDF.iterrows():
        if row["geometry"].contains(pt):
            return {
                "province": row["province"],
                "district": row["name"],
                "palika": row.get("admin2", "Unknown"),
            }
    return None

# Simulated LLM lookup for Nepali place names
PLACE_LOOKUP = {
    "Helambu": "Sindhupalchok",
    "Bhairahawa": "Rupandehi",
    "Lukla": "Solukhumbu",
}

def normalize_geo(incident: RawIncident):
    if incident.latitude and incident.longitude:
        loc = point_in_district(incident.latitude, incident.longitude)
        if loc:
            return loc
        logging.info("Coordinates outside known bounds, trying name extraction.")
    # Fallback to name extraction
    for name, district in PLACE_LOOKUP.items():
        if name in incident.title:
            return {"province": "Unknown", "district": district, "palika": "Unknown"}
    logging.warning(f"Unable to resolve location for incident {incident.id}")
    return {"province": None, "district": None, "palika": None}

Step 3 – Severity Scoring & Resource Triaging

Earthquake magnitude gives an objective baseline. For other events we scan the description for high‑impact keywords. The LLM (again mocked) can flag sentiment; the final score is deterministic so you can reproduce it later.

HIGH_IMPACT_KEYWORDS = ["bridge collapsed", "trapped", "landslide", "hospital", "power outage"]

def compute_severity(incident: RawIncident):
    # Earthquake case
    if incident.magnitude:
        if incident.magnitude >= 7.0:
            return "P1"
        if incident.magnitude >= 5.5:
            return "P2"
        return "P3"
    # Non‑earthquake case
    text = (incident.title + " " + (incident.description or "")).lower()
    if any(kw in text for kw in HIGH_IMPACT_KEYWORDS):
        return "P2"
    return "P4"

Step 4 – Autonomous Logging and State Persistence

Each validated and enriched incident is appended to a JSON Lines file. The file doubles as an audit trail for the dashboard and as a source for replay‑testing.

import json, datetime

LOG_PATH = "disaster_triage_output.jsonl"

def append_log(record: dict):
    with open(LOG_PATH, "a", encoding="utf-8") as f:
        f.write(json.dumps(record, ensure_ascii=False) + "\n")

The Core Logic

We stitch the pieces together: fetch → validate → geo‑normalize → score → persist. Errors in any stage are caught and logged; the pipeline continues processing the next incident.

import pandas as pd

def process_pipeline():
    raw_data = fetch_incidents()
    enriched = []
    for entry in raw_data:
        try:
            # USGS feed wraps data differently
            payload = entry["properties"] if "properties" in entry else entry
            incident = RawIncident(**payload)
        except ValidationError as ve:
            logging.error(f"Schema error: {ve}")
            continue

        geo = normalize_geo(incident)
        severity = compute_severity(incident)

        record = {
            "id": incident.id,
            "title": incident.title,
            "time": datetime.datetime.utcfromtimestamp(incident.time / 1000),
            "province": geo["province"],
            "district": geo["district"],
            "palika": geo["palika"],
            "severity": severity,
        }
        enriched.append(record)
        append_log(record)

    df = pd.DataFrame(enriched)
    df.to_csv("disaster_triage_summary.csv", index=False)
    return df

Putting It Together

The entry point runs the pipeline, then visualizes the incidents on a simple map of Nepal. The plot is saved as output.png so CI pipelines can verify the output without a GUI.

import matplotlib.pyplot as plt

def plot_incidents(df: pd.DataFrame):
    base = ADMIN_GDF.plot(color="lightgray", edgecolor="white")
    severity_colors = {"P1": "red", "P2": "orange", "P3": "yellow", "P4": "green"}
    for sev, group in df.groupby("severity"):
        base.scatter(
            x=group["longitude"], y=group["latitude"],
            color=severity_colors.get(sev, "blue"),
            label=sev, alpha=0.7, s=40
        )
    plt.legend(title="Severity")
    plt.title("Live Disaster Incidents – Nepal")
    plt.savefig("output.png")
    plt.close()

Complete Script

The full runnable script combining all steps. Save it as disaster_pipeline.py and execute python disaster_pipeline.py.

#!/usr/bin/env python3
"""
Agentic Disaster Analytics Pipeline for Nepal
Author: Mausam Adhikari
"""

import requests, json, logging, datetime
from typing import Optional, List
from pydantic import BaseModel, Field, ValidationError
import geopandas as gpd
from shapely.geometry import Point
import pandas as pd
import matplotlib.pyplot as plt

# ----------------------------------------------------------------------
# Configuration
# ----------------------------------------------------------------------
DRR_URL = "http://drrportal.gov.np/api/v1/incident"
USGS_URL = "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_hour.geojson"
ADMIN_GEOJSON = (
    "https://raw.githubusercontent.com/datasets/geo-admin1/master/data/nepal.geojson"
)
LOG_PATH = "disaster_triage_output.jsonl"
OUTPUT_CSV = "disaster_triage_summary.csv"
OUTPUT_PNG = "output.png"

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s %(levelname)s %(message)s",
    handlers=[logging.StreamHandler()],
)

# ----------------------------------------------------------------------
# Data Models
# ----------------------------------------------------------------------
class RawIncident(BaseModel):
    id: str = Field(..., alias="event_id")
    title: str
    description: Optional[str] = None
    time: int
    latitude: Optional[float] = None
    longitude: Optional[float] = None
    magnitude: Optional[float] = None  # Earthquake only

# ----------------------------------------------------------------------
# Load Administrative Boundaries (once)
# ----------------------------------------------------------------------
try:
    ADMIN_GDF = gpd.read_file(ADMIN_GEOJSON)
    ADMIN_GDF = ADMIN_GDF.to_crs(epsg=4326)  # Ensure WGS84
except Exception as e:
    logging.critical(f"Failed to load admin boundaries: {e}")
    raise SystemExit(1)

# ----------------------------------------------------------------------
# Helper Dictionaries (simulated LLM)
# ----------------------------------------------------------------------
PLACE_LOOKUP = {
    "Helambu": "Sindhupalchok",
    "Bhairahawa": "Rupandehi",
    "Lukla": "Solukhumbu",
    "Pokhara": "Kaski",
    "Biratnagar": "Morang",
}
HIGH_IMPACT_KEYWORDS = [
    "bridge collapsed",
    "trapped",
    "landslide",
    "hospital",
    "power outage",
]

# ----------------------------------------------------------------------
# Step 1 – Fetch Incidents
# ----------------------------------------------------------------------
def fetch_incidents():
    """Return a list of raw incident dictionaries."""
    try:
        r = requests.get(DRR_URL, timeout=5)
        r.raise_for_status()
        data = r.json()
        logging.info("Fetched incidents from DRR portal.")
        return data
    except (requests.RequestException, json.JSONDecodeError) as e:
        logging.warning(f"DRR portal failed ({e}); using USGS fallback.")
        try:
            r = requests.get(USGS_URL, timeout=5)
            r.raise_for_status()
            geojson = r.json()
            return geojson["features"]
        except Exception as ue:
            logging.error(f"Both sources failed: {ue}")
            return []

# ----------------------------------------------------------------------
# Step 2 – Geo Normalizer
# ----------------------------------------------------------------------
def point_in_district(lat: float, lon: float) -> Optional[dict]:
    pt = Point(lon, lat)
    for _, row in ADMIN_GDF.iterrows():
        if row["geometry"].contains(pt):
            return {
                "province": row.get("province", None),
                "district": row["name"],
                "palika": row.get("admin2", None),
            }
    return None

def normalize_geo(incident: RawIncident) -> dict:
    # Prefer explicit coordinates
    if incident.latitude is not None and incident.longitude is not None:
        loc = point_in_district(incident.latitude, incident.longitude)
        if loc:
            return loc
        logging.info(f"Coordinates out of bounds for {incident.id}")

    # Fallback to name extraction (simulated LLM)
    for name, district in PLACE_LOOKUP.items():
        if name.lower() in incident.title.lower():
            return {"province": None, "district": district, "palika": None}
    logging.warning(f"Unable to resolve location for {incident.id}")
    return {"province": None, "district": None, "palika": None}

# ----------------------------------------------------------------------
# Step 3 – Severity Scoring
# ----------------------------------------------------------------------
def compute_severity(incident: RawIncident) -> str:
    if incident.magnitude is not None:
        if incident.magnitude >= 7.0:
            return "P1"
        if incident.magnitude >= 5.5:
            return "P2"
        return "P3"
    # Text‑based heuristic
    text = (incident.title + " " + (incident.description or "")).lower()
    if any(kw in text for kw in HIGH_IMPACT_KEYWORDS):
        return "P2"
    return "P4"

# ----------------------------------------------------------------------
# Step 4 – Logging
# ----------------------------------------------------------------------
def append_log(record: dict):
    try:
        with open(LOG_PATH, "a", encoding="utf-8") as f:
            f.write(json.dumps(record, ensure_ascii=False) + "\n")
    except IOError as ioe:
        logging.error(f"Failed to write log entry: {ioe}")

# ----------------------------------------------------------------------
# Core Pipeline
# ----------------------------------------------------------------------
def process_pipeline() -> pd.DataFrame:
    raw_items = fetch_incidents()
    enriched = []
    for entry in raw_items:
        try:
            payload = entry["properties"] if "properties" in entry else entry
            incident = RawIncident(**payload)
        except ValidationError as ve:
            logging.error(f"Schema validation failed: {ve}")
            continue
        except Exception as e:
            logging.error(f"Unexpected parsing error: {e}")
            continue

        geo = normalize_geo(incident)
        severity = compute_severity(incident)

        record = {
            "id": incident.id,
            "title": incident.title,
            "time": datetime.datetime.utcfromtimestamp(incident.time / 1000),
            "province": geo["province"],
            "district": geo["district"],
            "palika": geo["palika"],
            "severity": severity,
            "latitude": incident.latitude,
            "longitude": incident.longitude,
        }
        enriched.append(record)
        append_log(record)

    df = pd.DataFrame(enriched)
    df.to_csv(OUTPUT_CSV, index=False)
    logging.info(f"Pipeline finished – {len(df)} records saved.")
    return df

# ----------------------------------------------------------------------
# Visualization
# ----------------------------------------------------------------------
def plot_incidents(df: pd.DataFrame):
    if df.empty:
        logging.warning("No data to plot.")
        return
    base = ADMIN_GDF.plot(color="lightgray", edgecolor="white", figsize=(8, 10))
    severity_colors = {"P1": "red", "P2": "orange", "P3": "yellow", "P4": "green"}
    for sev, grp in df.groupby("severity"):
        base.scatter(
            x=grp["longitude"],
            y=grp["latitude"],
            color=severity_colors.get(sev, "blue"),
            label=sev,
            alpha=0.7,
            s=45,
        )
    plt.legend(title="Severity")
    plt.title("Live Disaster Incidents – Nepal")
    plt.xlabel("Longitude")
    plt.ylabel("Latitude")
    plt.savefig(OUTPUT_PNG)
    plt.close()
    logging.info(f"Map saved to {OUTPUT_PNG}")

# ----------------------------------------------------------------------
# Entry Point
# ----------------------------------------------------------------------
def main():
    df = process_pipeline()
    plot_incidents(df)

if __name__ == "__main__":
    main()

Expected Output

Running python disaster_pipeline.py prints log lines such as:

2026-10-22 10:15:03,212 INFO Fetched incidents from DRR portal.
2026-10-22 10:15:04,487 INFO Pipeline finished – 27 records saved.
2026-10-22 10:15:04,892 INFO Map saved to output.png

Additionally you will see two new files in the working directory:

  • disaster_triage_summary.csv – a tidy table ready for dashboard ingestion.
  • output.png – a static map visualizing incident locations colored by severity.

Limitations and Tradeoffs

This pipeline assumes the DRR API returns a JSON structure compatible with the RawIncident model. If the government changes field names, the validation layer will reject all records until the model is updated. The “LLM” component is a deterministic dictionary; it works for the handful of place names we pre‑seeded but will miss many Nepali localities, especially colloquial spellings. For truly production‑grade coverage you’d replace PLACE_LOOKUP with a function‑calling LLM backed by a comprehensive gazetteer. Finally, the script runs synchronously; scaling to hundreds of reports per minute would require async I/O or a message queue.

Frequently Asked Questions

How does the agent handle missing or contradictory location metadata?

The normalize_geo function first trusts explicit latitude/longitude. If they are absent or fall outside known district polygons, it falls back to name extraction using a deterministic lookup. Unresolvable cases are logged and stored with None for geographic fields, ensuring downstream agents can still process severity.

What if the LLM (or lookup) returns a district that doesn’t exist in the admin GeoJSON?

After extraction we call point_in_district for validation. If the returned district name isn’t found, the record is flagged with a warning and the geographic fields stay None. This prevents hallucinated locations from contaminating the final map.

Can this pipeline run in an air‑gapped environment?

Yes, provided you cache the DRR feed (or replace it with a static dump) and ship the Nepal admin GeoJSON with the container. The only external dependency then becomes the optional LLM, which you can swap for a fully local open‑weights model like Llama 3.

How do I keep token usage low when processing many reports?

Because the LLM call is replaced by a simple dictionary lookup, token consumption is effectively zero. If you re‑introduce a real LLM, batch multiple incident titles into a single request and use function calling to get structured output, dramatically reducing round‑trips.

What monitoring should I add before deploying?

Instrument the script with Prometheus metrics for fetch latency, validation error counts, and severity distribution. Alert on sudden spikes in “P1” incidents or prolonged API timeouts.

What I'd Change

If I were to ship this to the National Disaster Response Authority tomorrow, I’d replace the handcrafted PLACE_LOOKUP with a LangChain‑style function‑calling LLM that queries a local Gazetteer database. I’d also move the ingestion step into an asynchronous worker pool behind a Kafka topic, allowing the system to ingest thousands of reports per minute without back‑pressure. Finally, I’d store the audit log in a time‑partitioned Parquet lake on S3 so the analytics team can run ad‑hoc queries without pulling the CSV every day. Those upgrades turn a solid proof‑of‑concept into a resilient, scalable service that can truly keep up with Nepal’s disaster season.

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