$0.01
per call · USD Coin on Base
Active and forecast flood risk (NWS + model) — Active NWS alerts filtered to flood hazards for any US point, with model-based risk context. Same shape as /v1/alerts/severe with the hazard class fixed to flood.
Endpoint
GET https://x402-factory.com/v1/alerts/flood1
Calls / 30d
1
Unique payers / 30d
Sep 7
Last called
exact
Payment scheme
Call this service
import { wrapFetchWithPayment } from "@x402/fetch";
import { privateKeyToAccount } from "viem/accounts";
const account = privateKeyToAccount(process.env.PRIVATE_KEY);
const fetchWithPay = wrapFetchWithPayment(fetch, account);
const res = await fetchWithPay("https://x402-factory.com/v1/alerts/flood", {
method: "GET",
});
const data = await res.json();curl -X GET \
"https://x402-factory.com/v1/alerts/flood?lat=33.4484&lon=-112.074" \
-H "X-PAYMENT: <signed x402 payment>"Payment details
0xcc42ed39a06b6cf3f484307ef76b88bf56fb305fUSD Coin · 0x833589fcd6edb6e08f4c7c32d4f71b54bda02913Is this your API?
Pin it to the top of Data & Enrichment and the homepage with a featured placement.
More from x402-factory & similar services
x402-factory · Evergreen Presidential Election Winner Party
x402-factory.com
Live probability: Which party wins the next US presidential election? — Stable evergreen URL for a recurring question: Which party wins the next US presidential election? Auto-resolves to the current market instance and rolls forward on resolution, so the answer is always about the next occurrence. Response adds current_slug and rolls_to_next_at to the standard probability shape.
x402-factory · Ping
x402-factory.com
Cheapest endpoint: liveness plus x402 payment pipeline check — Returns ok with the server time and whether this call was paid. Use it to verify your x402 client end to end before spending on data endpoints.
x402-factory · Edge Backtest
x402-factory.com
Historical performance of the edge signal: hit rate, calibration, by hazard — Aggregates stored edge signals whose markets have resolved: how often the model beat the market (hit rate), average absolute-error improvement (edge captured), a model calibration curve, and per-hazard breakdowns. model_label is explicit about which model produced the signals.