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Google Flood Forecasting
========================

Overview
--------

``google_flood_forecasting`` is a compact sequence-to-node forecasting baseline for flood streamflow prediction.

At a Glance
-----------

.. grid:: 1 2 4 4
   :gutter: 2
   :class-container: catalog-grid

   .. grid-item-card:: Hazard Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Flood

      .. container:: catalog-stat-note

         Public catalog grouping used for this model.

   .. grid-item-card:: Maturity
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Implemented

      .. container:: catalog-stat-note

         Catalog maturity label used on the index page.

   .. grid-item-card:: Tasks
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         1

      .. container:: catalog-stat-note

         Streamflow

   .. grid-item-card:: Benchmark Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         :doc:`Flood Benchmark </benchmarks/flood_benchmark>`

      .. container:: catalog-stat-note

         Primary benchmark-family link used for compatible evaluation coverage.


Description
-----------

``google_flood_forecasting`` is a compact sequence-to-node forecasting baseline for flood streamflow prediction.

The PyHazards implementation uses a transformer encoder over per-node history windows and returns one forecast value per node.

Benchmark Compatibility
-----------------------

**Primary benchmark family:** :doc:`Flood Benchmark </benchmarks/flood_benchmark>`

**Mapped benchmark ecosystems:** :doc:`Caravan </benchmarks/caravan>`

External References
-------------------

**Paper:** `Global Flood Forecasting at a Fine Catchment Resolution using Machine Learning <https://research.google/pubs/global-flood-forecasting-at-a-fine-catchment-resolution-using-machine-learning/>`_ | **Repo:** `Repository <https://github.com/google-research/flood-forecasting>`__

Registry Name
-------------

Primary entrypoint: ``google_flood_forecasting``

Supported Tasks
---------------

- Streamflow

Programmatic Use
----------------

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(
       name="google_flood_forecasting",
       task="regression",
       input_dim=2,
       out_dim=1,
       history=4,
   )
   preds = model({"x": torch.randn(2, 4, 6, 2)})
   print(preds.shape)

Notes
-----

- The smoke path uses the same streamflow-style graph fixture as the other flood baselines.