Item talk:Q300823
From geokb
{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70254341", "url": "https://pubs.usgs.gov/publication/70254341" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70254341 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1016/j.advwatres.2023.104440", "url": "https://doi.org/10.1016/j.advwatres.2023.104440" } ], "journal": { "@type": "Periodical", "name": "Advances in Water Resources", "volumeNumber": "175", "issueNumber": null }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "Advances in Water Resources" } ], "datePublished": "2023", "dateModified": "2024-05-20", "abstract": "Prediction of short- (i.e., aquifer is near or at saturated conditions) and long-time (i.e., aquifer is not near or at saturated conditions) baseflow recession characteristics at ungauged stream locations is a current challenge that has been primarily addressed by empirical approaches that relate these characteristics to basin attributes. However, the performance of these models is often only fair with coefficient of determination values ranging from 0.5 to 0.7. In this study, we propose a hybrid physical and machine learning approach to predict the long- and short-time baseflow recession characteristics at ungauged stream locations. This approach is compared to a machine learning method, random forest regression, that relates baseflow recession characteristics to basin attributes in 582 basins across the western and eastern United States. The new approach resulted in lower median and inner quartile ranges (IQR) of absolute normalized errors in predicting long-time baseflow recession characteristics (western: 23%, IQR=32%; eastern: 30%, IQR=39%) compared to estimates of those properties based on random forest regressions (western: 27%, IQR=34%; eastern: 38%, IQR=50%). For the short-time baseflow recession characteristics, the hybrid approach resulted in substantially lower median errors and IQR values (western: 79%, IQR=143%; eastern: 83%, IQR=140%) compared to estimates from random forest regressions (western: 1,577%, IQR=8,887%; eastern: 341%, IQR=2,154%). In addition, this approach identified four major regions in the western United States and three in the eastern United States where the baseflow recession characteristics are mostly constant, and these characteristics only vary based on the geometric properties of aquifers. Lastly, the inter-basin variability of the baseflow recession characteristics was not found to be strongly related to metrics measuring interstorm arrival periods, average number of storms, and average length of storms.", "description": "104440", "publisher": { "@type": "Organization", "name": "Elsevier" }, "author": [ { "@type": "Person", "name": "Eng, Ken keng@usgs.gov", "givenName": "Ken", "familyName": "Eng", "email": "keng@usgs.gov", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0001-6838-5849", "url": "https://orcid.org/0000-0001-6838-5849" }, "affiliation": [ { "@type": "Organization", "name": "National Research Program - Eastern Branch", "url": "https://www.usgs.gov/centers/arizona-water-science-center" }, { "@type": "Organization", "name": "WMA - Integrated Modeling and Prediction Division", "url": "https://www.usgs.gov/mission-areas/water-resources" } ] }, { "@type": "Person", "name": "Wolock, David M.", "givenName": "David M.", "familyName": "Wolock", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-6209-938X", "url": "https://orcid.org/0000-0002-6209-938X" }, "affiliation": [ { "@type": "Organization", "name": "WMA - Integrated Modeling and Prediction Division", "url": "https://www.usgs.gov/mission-areas/water-resources" } ] }, { "@type": "Person", "name": "Wieczorek, Michael", "givenName": "Michael", "familyName": "Wieczorek", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0003-0999-5457", "url": "https://orcid.org/0000-0003-0999-5457" }, "affiliation": [ { "@type": "Organization", "name": "National Water Quality Assessment Program", "url": "https://www.usgs.gov/programs/national-water-quality-program" }, { "@type": "Organization", "name": "Lower Mississippi-Gulf Water Science Center", "url": "https://www.usgs.gov/centers/lower-mississippi-gulf-water-science-center" }, { "@type": "Organization", "name": "Maryland Water Science Center", "url": "https://www.usgs.gov/centers/md-de-dc-water" }, { "@type": "Organization", "name": "National Water Quality Program", "url": "https://www.usgs.gov/programs/national-water-quality-program" } ] } ], "funder": [ { "@type": "Organization", "name": "WMA - Integrated Modeling and Prediction Division", "url": "https://www.usgs.gov/mission-areas/water-resources" } ] }, "OpenAlex": { "_id": "https://openalex.org/w4362721423", "abstract_inverted_index": { "Prediction": [ 0 ], "of": [ 1, 51, 60, 135, 154, 243, 249, 269, 274 ], "short-": [ 2 ], "(i.e.,": [ 3, 13 ], "aquifer": [ 4, 14 ], "is": [ 5, 15, 29, 54, 95 ], "near": [ 6, 17 ], "or": [ 7, 18 ], "at": [ 8, 19, 25, 89 ], "saturated": [ 9, 20 ], "conditions)": [ 10, 21 ], "and": [ 11, 76, 84, 119, 130, 183, 218, 233, 271 ], "long-time": [ 12, 141 ], "not": [ 16, 255 ], "baseflow": [ 22, 86, 107, 142, 171, 227, 251 ], "recession": [ 23, 87, 108, 143, 172, 228, 252 ], "characteristics": [ 24, 44, 88, 109, 144, 229, 235, 253 ], "ungauged": [ 26, 90 ], "stream": [ 27, 91 ], "locations": [ 28 ], "a": [ 30, 73, 98 ], "current": [ 31 ], "challenge": [ 32 ], "that": [ 33, 41, 105 ], "has": [ 34 ], "been": [ 35 ], "primarily": [ 36 ], "addressed": [ 37 ], "by": [ 38 ], "empirical": [ 39 ], "approaches": [ 40 ], "relate": [ 42 ], "these": [ 43, 52, 234 ], "to": [ 45, 66, 80, 97, 110, 152, 193, 257, 261 ], "basin": [ 46, 111 ], "attributes.": [ 47 ], "However,": [ 48 ], "the": [ 49, 82, 117, 169, 174, 214, 221, 226, 240, 246, 250 ], "performance": [ 50 ], "models": [ 53 ], "often": [ 55 ], "only": [ 56, 236 ], "fair": [ 57 ], "with": [ 58 ], "coefficient": [ 59 ], "determination": [ 61 ], "values": [ 62, 185 ], "ranging": [ 63 ], "from": [ 64, 195 ], "0.5": [ 65 ], "0.7.": [ 67 ], "In": [ 68, 205 ], "this": [ 69, 207 ], "study,": [ 70 ], "we": [ 71 ], "propose": [ 72 ], "hybrid": [ 74, 175 ], "physical": [ 75 ], "machine": [ 77, 99 ], "learning": [ 78, 100 ], "approach": [ 79, 94, 125, 176, 208 ], "predict": [ 81 ], "long-": [ 83 ], "short-time": [ 85, 170 ], "locations.": [ 92 ], "This": [ 93 ], "compared": [ 96, 151, 192 ], "method,": [ 101 ], "random": [ 102, 159, 196 ], "forest": [ 103, 160, 197 ], "regression,": [ 104 ], "relates": [ 106 ], "attributes": [ 112 ], "in": [ 113, 127, 139, 178, 213, 220 ], "582": [ 114 ], "basins": [ 115 ], "across": [ 116 ], "western": [ 118, 215 ], "eastern": [ 120, 222 ], "United": [ 121, 216, 223 ], "States.": [ 122 ], "The": [ 123 ], "new": [ 124 ], "resulted": [ 126, 177 ], "lower": [ 128, 180 ], "median": [ 129, 181 ], "inner": [ 131 ], "quartile": [ 132 ], "ranges": [ 133 ], "(IQR)": [ 134 ], "absolute": [ 136 ], "normalized": [ 137 ], "errors": [ 138, 182 ], "predicting": [ 140 ], "(western:": [ 145, 162, 186, 199 ], "23%,": [ 146 ], "IQR=32%;": [ 147 ], "eastern:": [ 148, 165, 189, 202 ], "30%,": [ 149 ], "IQR=39%)": [ 150 ], "estimates": [ 153, 194 ], "those": [ 155 ], "properties": [ 156, 242 ], "based": [ 157, 238 ], "on": [ 158, 239 ], "regressions": [ 161, 198 ], "27%,": [ 163 ], "IQR=34%;": [ 164 ], "38%,": [ 166 ], "IQR=50%).": [ 167 ], "For": [ 168 ], "characteristics,": [ 173 ], "substantially": [ 179 ], "IQR": [ 184 ], "79%,": [ 187 ], "IQR=143%;": [ 188 ], "83%,": [ 190 ], "IQR=140%)": [ 191 ], "1,577%,": [ 200 ], "IQR=8,887%;": [ 201 ], "341%,": [ 203 ], "IQR=2,154%).": [ 204 ], "addition,": [ 206 ], "identified": [ 209 ], "four": [ 210 ], "major": [ 211 ], "regions": [ 212 ], "States": [ 217, 224 ], "three": [ 219 ], "where": [ 225 ], "are": [ 230 ], "mostly": [ 231 ], "constant,": [ 232 ], "vary": [ 237 ], "geometric": [ 241 ], "aquifers.": [ 244 ], "Lastly,": [ 245 ], "inter-basin": [ 247 ], "variability": [ 248 ], "was": [ 254 ], "found": [ 256 ], "be": [ 258 ], "strongly": [ 259 ], "related": [ 260 ], "metrics": [ 262 ], "measuring": [ 263 ], "interstorm": [ 264 ], "arrival": [ 265 ], "periods,": [ 266 ], "average": [ 267, 272 ], "number": [ 268 ], "storms,": [ 270 ], "length": [ 273 ], "storms.": [ 275 ] }, "apc_list": { "value": 3730, "currency": "USD", "value_usd": 3730, "provenance": "doaj" }, "apc_paid": null, "authorships": [ { "author_position": "first", "author": { "id": "https://openalex.org/A5028436152", "display_name": "Ken Eng", "orcid": "https://orcid.org/0000-0001-6838-5849" }, "institutions": [ { "id": "https://openalex.org/I1286329397", "display_name": "United States Geological Survey", "ror": "https://ror.org/035a68863", "country_code": "US", "type": "government", "lineage": [ "https://openalex.org/I1286329397", "https://openalex.org/I1335927249" ] } ], "countries": [ "US" ], "is_corresponding": true, "raw_author_name": "Ken Eng", "raw_affiliation_strings": [ "Integrated Modeling and Prediction Division, U.S. Geological Survey, 12201 Sunrise Valley Drive, Mail Stop 430, Reston, VA 20192, USA" ], "affiliations": [ { "raw_affiliation_string": "Integrated Modeling and Prediction Division, U.S. Geological Survey, 12201 Sunrise Valley Drive, Mail Stop 430, Reston, VA 20192, USA", "institution_ids": [ "https://openalex.org/I1286329397" ] } ] }, { "author_position": "middle", "author": { "id": "https://openalex.org/A5067132821", "display_name": "David M. Wolock", "orcid": "https://orcid.org/0000-0002-6209-938X" }, "institutions": [ { "id": "https://openalex.org/I1286329397", "display_name": "United States Geological Survey", "ror": "https://ror.org/035a68863", "country_code": "US", "type": "government", "lineage": [ "https://openalex.org/I1286329397", "https://openalex.org/I1335927249" ] } ], "countries": [ "US" ], "is_corresponding": false, "raw_author_name": "David M. Wolock", "raw_affiliation_strings": [ "Integrated Modeling and Prediction Division, U.S. Geological Survey, 4821 Quail Crest Place, Lawrence, KS 66049, USA" ], "affiliations": [ { "raw_affiliation_string": "Integrated Modeling and Prediction Division, U.S. Geological Survey, 4821 Quail Crest Place, Lawrence, KS 66049, USA", "institution_ids": [ "https://openalex.org/I1286329397" ] } ] }, { "author_position": "last", "author": { "id": "https://openalex.org/A5049096257", "display_name": "Michael Wieczorek", "orcid": "https://orcid.org/0000-0003-0999-5457" }, "institutions": [ { "id": "https://openalex.org/I1286329397", "display_name": "United States Geological Survey", "ror": "https://ror.org/035a68863", "country_code": "US", "type": "government", "lineage": [ "https://openalex.org/I1286329397", "https://openalex.org/I1335927249" ] } ], "countries": [ "US" ], "is_corresponding": false, "raw_author_name": "Michael Wieczorek", "raw_affiliation_strings": [ "Integrated Modeling and Prediction Division, U.S. Geological Survey, 5522 Research Park Drive, Catonsville, MD 21228, USA" ], "affiliations": [ { "raw_affiliation_string": "Integrated Modeling and Prediction Division, U.S. Geological Survey, 5522 Research Park Drive, Catonsville, MD 21228, USA", "institution_ids": [ "https://openalex.org/I1286329397" ] } ] } ], "best_oa_location": null, "biblio": { "volume": "175", "issue": null, "first_page": "104440", "last_page": "104440" }, "citation_normalized_percentile": null, "cited_by_api_url": "https://api.openalex.org/works?filter=cites:W4362721423", "cited_by_count": 0, "cited_by_percentile_year": { "min": 0, "max": 74 }, "concepts": [ { "id": "https://openalex.org/C76856003", "wikidata": "https://www.wikidata.org/wiki/Q30036", "display_name": "Baseflow", "level": 4, "score": 0.9759477 }, { "id": "https://openalex.org/C195742910", "wikidata": "https://www.wikidata.org/wiki/Q176494", "display_name": "Recession", "level": 2, "score": 0.725332 }, { "id": "https://openalex.org/C169258074", "wikidata": "https://www.wikidata.org/wiki/Q245748", "display_name": "Random forest", "level": 2, "score": 0.5472767 }, { "id": "https://openalex.org/C109007969", "wikidata": "https://www.wikidata.org/wiki/Q749565", "display_name": "Structural basin", "level": 2, "score": 0.5115765 }, { "id": "https://openalex.org/C68443243", "wikidata": "https://www.wikidata.org/wiki/Q2786686", "display_name": "Quartile", "level": 3, "score": 0.49879074 }, { "id": "https://openalex.org/C39432304", "wikidata": "https://www.wikidata.org/wiki/Q188847", "display_name": "Environmental science", "level": 0, "score": 0.49262744 }, { "id": "https://openalex.org/C105795698", "wikidata": "https://www.wikidata.org/wiki/Q12483", "display_name": "Statistics", "level": 1, "score": 0.41782302 }, { "id": "https://openalex.org/C83546350", "wikidata": "https://www.wikidata.org/wiki/Q1139051", "display_name": "Regression", "level": 2, "score": 0.41357917 }, { "id": "https://openalex.org/C126645576", "wikidata": "https://www.wikidata.org/wiki/Q166620", "display_name": "Drainage basin", "level": 2, "score": 0.40638983 }, { "id": "https://openalex.org/C76886044", "wikidata": "https://www.wikidata.org/wiki/Q2883300", "display_name": "Hydrology (agriculture)", "level": 2, "score": 0.36859858 }, { "id": "https://openalex.org/C149782125", "wikidata": "https://www.wikidata.org/wiki/Q160039", "display_name": "Econometrics", "level": 1, "score": 0.35355267 }, { "id": "https://openalex.org/C127313418", "wikidata": "https://www.wikidata.org/wiki/Q1069", "display_name": "Geology", "level": 0, "score": 0.3116318 }, { "id": "https://openalex.org/C205649164", "wikidata": "https://www.wikidata.org/wiki/Q1071", "display_name": "Geography", "level": 0, "score": 0.219915 }, { "id": "https://openalex.org/C53739315", "wikidata": "https://www.wikidata.org/wiki/Q29425295", "display_name": "Streamflow", "level": 3, "score": 0.18708888 }, { "id": "https://openalex.org/C33923547", "wikidata": "https://www.wikidata.org/wiki/Q395", "display_name": "Mathematics", "level": 0, "score": 0.16865668 }, { "id": "https://openalex.org/C119857082", "wikidata": "https://www.wikidata.org/wiki/Q2539", "display_name": "Machine learning", "level": 1, "score": 0.15227729 }, { "id": "https://openalex.org/C41008148", "wikidata": "https://www.wikidata.org/wiki/Q21198", "display_name": "Computer science", "level": 0, "score": 0.1521151 }, { "id": "https://openalex.org/C114793014", "wikidata": "https://www.wikidata.org/wiki/Q52109", "display_name": "Geomorphology", "level": 1, "score": 0.12835613 }, { "id": "https://openalex.org/C58640448", "wikidata": "https://www.wikidata.org/wiki/Q42515", "display_name": "Cartography", "level": 1, "score": 0.115351856 }, { "id": "https://openalex.org/C162324750", "wikidata": "https://www.wikidata.org/wiki/Q8134", "display_name": "Economics", "level": 0, "score": 0.06458101 }, { "id": "https://openalex.org/C187320778", "wikidata": "https://www.wikidata.org/wiki/Q1349130", "display_name": "Geotechnical engineering", "level": 1, "score": 0.062871605 }, { "id": "https://openalex.org/C165556158", "wikidata": "https://www.wikidata.org/wiki/Q83937", "display_name": "Keynesian economics", "level": 1, "score": 0.0 }, { "id": "https://openalex.org/C44249647", "wikidata": "https://www.wikidata.org/wiki/Q208498", "display_name": "Confidence interval", "level": 2, "score": 0.0 } ], "corresponding_author_ids": [ "https://openalex.org/A5028436152" ], "corresponding_institution_ids": [ "https://openalex.org/I1286329397" ], "countries_distinct_count": 1, "counts_by_year": [], "created_date": "2023-04-09", "datasets": [], "display_name": "Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach", "doi": "https://doi.org/10.1016/j.advwatres.2023.104440", "fwci": 0.0, "grants": [], "has_fulltext": false, "id": "https://openalex.org/W4362721423", "ids": { "openalex": "https://openalex.org/W4362721423", "doi": "https://doi.org/10.1016/j.advwatres.2023.104440" }, "indexed_in": [ "crossref" ], "institutions_distinct_count": 1, "is_paratext": false, "is_retracted": false, "keywords": [ { "id": "https://openalex.org/keywords/baseflow", "display_name": "Baseflow", "score": 0.9759477 }, { "id": "https://openalex.org/keywords/rainfall-runoff-modeling", "display_name": "Rainfall-Runoff Modeling", "score": 0.611287 }, { "id": "https://openalex.org/keywords/watershed-simulation", "display_name": "Watershed Simulation", "score": 0.580199 }, { "id": "https://openalex.org/keywords/hydrological-modeling", "display_name": "Hydrological Modeling", "score": 0.570189 }, { "id": "https://openalex.org/keywords/groundwater-level-forecasting", "display_name": "Groundwater Level Forecasting", "score": 0.556174 }, { "id": "https://openalex.org/keywords/forecasting", "display_name": "Forecasting", "score": 0.539106 }, { "id": "https://openalex.org/keywords/quartile", "display_name": "Quartile", "score": 0.49879074 } ], "language": "en", "locations": [ { "is_oa": false, "landing_page_url": "https://doi.org/10.1016/j.advwatres.2023.104440", "pdf_url": null, "source": { "id": "https://openalex.org/S145524021", "display_name": "Advances in Water Resources", "issn_l": "0309-1708", "issn": [ "0309-1708", "1872-9657" ], "is_oa": false, "is_in_doaj": false, "is_core": true, "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": [ "https://openalex.org/P4310320990" ], "host_organization_lineage_names": [ "Elsevier BV" ], "type": "journal" }, "license": null, "license_id": null, "version": null, "is_accepted": false, "is_published": false } ], "locations_count": 1, "mesh": [], "ngrams_url": "https://api.openalex.org/works/W4362721423/ngrams", "open_access": { "is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false }, "primary_location": { "is_oa": false, "landing_page_url": "https://doi.org/10.1016/j.advwatres.2023.104440", "pdf_url": null, "source": { "id": "https://openalex.org/S145524021", "display_name": "Advances in Water Resources", "issn_l": "0309-1708", "issn": [ "0309-1708", "1872-9657" ], "is_oa": false, "is_in_doaj": false, "is_core": true, "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": [ "https://openalex.org/P4310320990" ], "host_organization_lineage_names": [ "Elsevier BV" ], "type": "journal" }, "license": null, "license_id": null, "version": null, "is_accepted": false, "is_published": false }, "primary_topic": { "id": "https://openalex.org/T10330", "display_name": "Hydrological Modeling and Water Resource Management", "score": 0.9998, "subfield": { "id": "https://openalex.org/subfields/2312", "display_name": "Water Science and Technology" }, "field": { "id": "https://openalex.org/fields/23", "display_name": "Environmental Science" }, "domain": { "id": "https://openalex.org/domains/3", "display_name": "Physical Sciences" } }, "publication_date": "2023-05-01", "publication_year": 2023, "referenced_works": [ "https://openalex.org/W152528411", "https://openalex.org/W1698971166", "https://openalex.org/W1795688558", "https://openalex.org/W1807677645", "https://openalex.org/W1904945945", "https://openalex.org/W1970987402", "https://openalex.org/W1971370460", "https://openalex.org/W1973408814", "https://openalex.org/W1976800650", "https://openalex.org/W1983724666", "https://openalex.org/W1989172564", "https://openalex.org/W1993653485", "https://openalex.org/W1995047010", "https://openalex.org/W1996020109", "https://openalex.org/W200110011", "https://openalex.org/W2002587905", "https://openalex.org/W2006687042", "https://openalex.org/W2009496216", "https://openalex.org/W2033904036", "https://openalex.org/W2037918450", "https://openalex.org/W2039814904", "https://openalex.org/W2039853153", "https://openalex.org/W2044553211", "https://openalex.org/W2047599401", "https://openalex.org/W2049183239", "https://openalex.org/W2067826543", "https://openalex.org/W2068746661", "https://openalex.org/W2072372385", "https://openalex.org/W2109729302", "https://openalex.org/W2118572537", "https://openalex.org/W2120634534", "https://openalex.org/W2127295018", "https://openalex.org/W2127776537", "https://openalex.org/W2139086914", "https://openalex.org/W2151933490", "https://openalex.org/W2156198369", "https://openalex.org/W2161548576", "https://openalex.org/W2162820796", "https://openalex.org/W2171789305", "https://openalex.org/W2178047388", "https://openalex.org/W2239362388", "https://openalex.org/W2263824596", "https://openalex.org/W2464753696", "https://openalex.org/W2468286264", "https://openalex.org/W2517987201", "https://openalex.org/W2738378263", "https://openalex.org/W2764158987", "https://openalex.org/W2885765604", "https://openalex.org/W2901744658", "https://openalex.org/W2911964244", "https://openalex.org/W2961721889", "https://openalex.org/W2980639605", "https://openalex.org/W2981905647", "https://openalex.org/W3000178821", "https://openalex.org/W3011373767", "https://openalex.org/W3041301740" ], "referenced_works_count": 56, "related_works": [ "https://openalex.org/W4289884158", "https://openalex.org/W4288365262", "https://openalex.org/W3217432596", "https://openalex.org/W2940614149", "https://openalex.org/W2787485953", "https://openalex.org/W2736917288", "https://openalex.org/W2373130656", "https://openalex.org/W2155329689", "https://openalex.org/W2051876798", "https://openalex.org/W2048488252" ], "sustainable_development_goals": [ { "display_name": "Life on land", "score": 0.64, "id": "https://metadata.un.org/sdg/15" } ], "title": "Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach", "topics": [ { "id": "https://openalex.org/T10330", "display_name": "Hydrological Modeling and Water Resource Management", "score": 0.9998, "subfield": { "id": "https://openalex.org/subfields/2312", "display_name": "Water Science and Technology" }, "field": { "id": "https://openalex.org/fields/23", "display_name": "Environmental Science" }, "domain": { "id": "https://openalex.org/domains/3", "display_name": "Physical Sciences" } }, { "id": "https://openalex.org/T10894", "display_name": "Groundwater Flow and Transport Modeling", "score": 0.9988, "subfield": { "id": "https://openalex.org/subfields/2305", "display_name": "Environmental Engineering" }, "field": { "id": "https://openalex.org/fields/23", "display_name": "Environmental Science" }, "domain": { "id": "https://openalex.org/domains/3", "display_name": "Physical Sciences" } }, { "id": "https://openalex.org/T11490", "display_name": "Hydrological Modeling using Machine Learning Methods", "score": 0.9969, "subfield": { "id": "https://openalex.org/subfields/2305", "display_name": "Environmental Engineering" }, "field": { "id": "https://openalex.org/fields/23", "display_name": "Environmental Science" }, "domain": { "id": "https://openalex.org/domains/3", "display_name": "Physical Sciences" } } ], "type": "article", "type_crossref": "journal-article", "updated_date": "2024-08-09T01:07:04.748967", "versions": [] }
}