Item talk:Q257873
From geokb
{
"USGS Publications Warehouse": {
"@context": "https://schema.org",
"@type": "Article",
"additionalType": "Journal Article",
"name": "Stream temperature prediction in a shifting environment: The influence of deep learning architecture",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse IndexID",
"value": "70250381",
"url": "https://pubs.usgs.gov/publication/70250381"
},
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse Internal ID",
"value": 70250381
},
{
"@type": "PropertyValue",
"propertyID": "DOI",
"value": "10.1029/2022WR033880",
"url": "https://doi.org/10.1029/2022WR033880"
}
],
"journal": {
"@type": "Periodical",
"name": "Water Resources Research",
"volumeNumber": "59",
"issueNumber": "4"
},
"inLanguage": "en",
"isPartOf": [
{
"@type": "CreativeWorkSeries",
"name": "Water Resources Research"
}
],
"datePublished": "2023",
"dateModified": "2023-12-06",
"abstract": "Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process-guided deep learning models for predicting stream temperature (a recurrent graph convolution network\u2014RGCN, and a temporal convolution graph model\u2014Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64\u00b0C and 1.65\u00b0C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.",
"description": "e2022WR033880, 19 p.",
"publisher": {
"@type": "Organization",
"name": "American Geophysical Union"
},
"author": [
{
"@type": "Person",
"name": "Topp, Simon Nemer",
"givenName": "Simon Nemer",
"familyName": "Topp",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0001-7741-5982",
"url": "https://orcid.org/0000-0001-7741-5982"
},
"affiliation": [
{
"@type": "Organization",
"name": "WMA - Integrated Information Dissemination Division",
"url": "https://www.usgs.gov/mission-areas/water-resources"
}
]
},
{
"@type": "Person",
"name": "Barclay, Janet R. jbarclay@usgs.gov",
"givenName": "Janet R.",
"familyName": "Barclay",
"email": "jbarclay@usgs.gov",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0003-1643-6901",
"url": "https://orcid.org/0000-0003-1643-6901"
},
"affiliation": [
{
"@type": "Organization",
"name": "New England Water Science Center",
"url": "https://www.usgs.gov/centers/new-england-water-science-center"
}
]
},
{
"@type": "Person",
"name": "Diaz, Jeremy Alejandro",
"givenName": "Jeremy Alejandro",
"familyName": "Diaz",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0001-7087-7949",
"url": "https://orcid.org/0000-0001-7087-7949"
},
"affiliation": [
{
"@type": "Organization",
"name": "WMA - Integrated Information Dissemination Division",
"url": "https://www.usgs.gov/mission-areas/water-resources"
}
]
},
{
"@type": "Person",
"name": "Sun, Alexander Y.",
"givenName": "Alexander Y.",
"familyName": "Sun",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0002-6365-8526",
"url": "https://orcid.org/0000-0002-6365-8526"
},
"affiliation": [
{
"@type": "Organization",
"name": "University of Texas at Austin"
}
]
},
{
"@type": "Person",
"name": "Jia, Xiaowei",
"givenName": "Xiaowei",
"familyName": "Jia",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0001-8544-5233",
"url": "https://orcid.org/0000-0001-8544-5233"
},
"affiliation": [
{
"@type": "Organization",
"name": "University of Minnesota"
}
]
},
{
"@type": "Person",
"name": "Lubin, Daniel",
"givenName": "Daniel",
"familyName": "Lubin"
},
{
"@type": "Person",
"name": "Sadler, Jeffrey M",
"givenName": "Jeffrey M",
"familyName": "Sadler",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0001-8776-4844",
"url": "https://orcid.org/0000-0001-8776-4844"
},
"affiliation": [
{
"@type": "Organization",
"name": "Oklahoma State University"
}
]
},
{
"@type": "Person",
"name": "Appling, Alison P. aappling@usgs.gov",
"givenName": "Alison P.",
"familyName": "Appling",
"email": "aappling@usgs.gov",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0003-3638-8572",
"url": "https://orcid.org/0000-0003-3638-8572"
},
"affiliation": [
{
"@type": "Organization",
"name": "Office of Water Information",
"url": "https://www.usgs.gov/mission-areas/water-resources"
}
]
}
],
"funder": [
{
"@type": "Organization",
"name": "New England Water Science Center",
"url": "https://www.usgs.gov/centers/new-england-water-science-center"
},
{
"@type": "Organization",
"name": "WMA - Integrated Information Dissemination Division",
"url": "https://www.usgs.gov/mission-areas/water-resources"
}
],
"spatialCoverage": [
{
"@type": "Place",
"additionalType": "country",
"name": "United States",
"url": "https://geonames.org/4074035"
},
{
"@type": "Place",
"additionalType": "unknown",
"name": "Delaware River Basin"
},
{
"@type": "Place",
"geo": [
{
"@type": "GeoShape",
"additionalProperty": {
"@type": "PropertyValue",
"name": "GeoJSON",
"value": {
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"properties": {},
"geometry": {
"coordinates": [
[
[
-75.07997541667648,
38.70906787639316
],
[
-74.80531721355018,
39.00778043156808
],
[
-74.33839826823872,
40.4450386444411
],
[
-73.72865705730113,
40.994591290300974
],
[
-73.7835886979261,
42.478350475454334
],
[
-75.40956526042564,
42.295772510663625
],
[
-75.42055158855078,
41.8349594674406
],
[
-76.34340315105082,
40.43667721449637
],
[
-75.78859358073888,
39.713504216020766
],
[
-75.76662092448927,
39.578152174338356
],
[
-75.66225080730156,
39.41283383409595
],
[
-75.50294904948888,
39.22584914314203
],
[
-75.47548322917642,
39.042631522344635
],
[
-75.33266096355176,
38.846103881559685
],
[
-75.07997541667648,
38.70906787639316
]
]
],
"type": "Polygon"
}
}
]
}
}
},
{
"@type": "GeoCoordinates",
"latitude": 40.82474625025234,
"longitude": -74.94442477150832
}
]
}
]
}
}
