{
  "id": "1911.09427",
  "version": "v1",
  "published": "2019-11-21T12:01:19.000Z",
  "updated": "2019-11-21T12:01:19.000Z",
  "title": "Accurate Hydrologic Modeling Using Less Information",
  "authors": [
    "Guy Shalev",
    "Ran El-Yaniv",
    "Daniel Klotz",
    "Frederik Kratzert",
    "Asher Metzger",
    "Sella Nevo"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. Recent developments in rainfall-runoff modeling, one of the prime challenges in hydrology, show the value of a joint model with shared representation in this important context. However, current state-of-the-art models depend on detailed and reliable attributes characterizing each site to help the model differentiate correctly between the behavior of different sites. This dependency can present a challenge in data-poor regions. In this paper, we show that we can replace the need for such location-specific attributes with a completely data-driven learned embedding, and match previous state-of-the-art results with less information.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-11-21T12:01:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "accurate hydrologic modeling",
      "information",
      "joint model",
      "current state-of-the-art models",
      "prime challenges"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}