{"id":"https://openalex.org/W3045564528","doi":"https://doi.org/10.3906/elk-2002-99","title":"On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach","display_name":"On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach","publication_year":2020,"publication_date":"2020-04-07","ids":{"openalex":"https://openalex.org/W3045564528","doi":"https://doi.org/10.3906/elk-2002-99","mag":"3045564528"},"language":"en","primary_location":{"id":"doi:10.3906/elk-2002-99","is_oa":false,"landing_page_url":"https://doi.org/10.3906/elk-2002-99","pdf_url":null,"source":{"id":"https://openalex.org/S32837994","display_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES","issn_l":"1300-0632","issn":["1300-0632","1303-6203"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318422","host_organization_name":"Scientific and Technological Research Council of Turkey (TUBITAK)","host_organization_lineage":["https://openalex.org/P4310318422"],"host_organization_lineage_names":["Scientific and Technological Research Council of Turkey (TUBITAK)"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING &amp; COMPUTER SCIENCES","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069662077","display_name":"Emrullah Acar","orcid":"https://orcid.org/0000-0002-1897-9830"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Emrullah ACAR","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5017496995","display_name":"Mehmet Sira\u00e7 \u00d6zerdem","orcid":"https://orcid.org/0000-0002-9368-8902"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mehmet Sira\u00e7 \u00d6ZERDEM","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5069662077"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5713,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.63718019,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"28","issue":"4","first_page":"2316","last_page":"2330"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9988999962806702,"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9901000261306763,"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/T10716","display_name":"Soil and Unsaturated Flow","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6613371968269348},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6540320515632629},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5999423265457153},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5865316390991211},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5684782862663269},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5184735655784607},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.5130449533462524},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5036894679069519},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4899815618991852},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4848124086856842},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4167398512363434},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3870503008365631},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3428020477294922}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6613371968269348},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6540320515632629},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5999423265457153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5865316390991211},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5684782862663269},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5184735655784607},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.5130449533462524},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5036894679069519},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4899815618991852},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4848124086856842},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4167398512363434},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3870503008365631},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3428020477294922},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3906/elk-2002-99","is_oa":false,"landing_page_url":"https://doi.org/10.3906/elk-2002-99","pdf_url":null,"source":{"id":"https://openalex.org/S32837994","display_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES","issn_l":"1300-0632","issn":["1300-0632","1303-6203"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318422","host_organization_name":"Scientific and Technological Research Council of Turkey (TUBITAK)","host_organization_lineage":["https://openalex.org/P4310318422"],"host_organization_lineage_names":["Scientific and Technological Research Council of Turkey (TUBITAK)"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING &amp; COMPUTER SCIENCES","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1992962589","https://openalex.org/W3032871857","https://openalex.org/W1743191351","https://openalex.org/W3104633800","https://openalex.org/W3023567978","https://openalex.org/W2069184433","https://openalex.org/W3044778482","https://openalex.org/W3040494141","https://openalex.org/W4384026392","https://openalex.org/W2072400776"],"abstract_inverted_index":{"Soil":[0],"water":[1,17],"content":[2],"(SWC)":[3],"performs":[4],"an":[5],"important":[6],"role":[7],"in":[8,58,117],"many":[9],"areas":[10],"including":[11],"agriculture,":[12],"drought":[13],"cases,":[14],"usage":[15],"of":[16,27,87,105,125,137,147,188],"resources,":[18],"hydrology,":[19],"crop":[20],"diseases":[21],"and":[22,110,119,140,150,154,177,230],"aerology.":[23],"However,":[24],"the":[25,28,99,103,130,134,141,165,186,191,203,227,244],"measurement":[26],"SWC":[29,80,93,228],"over":[30,81,98,226],"large":[31,82],"terrains":[32,101],"with":[33,102,216],"standard":[34,62],"computational":[35],"techniques":[36,109],"is":[37,90],"very":[38],"hard.":[39],"In":[40,129,164,190],"order":[41],"to":[42,91,202,238,243],"overcome":[43],"this":[44,88,239],"situation,":[45],"remote":[46,67],"sensing":[47,68],"tools":[48],"are":[49],"preferred,":[50],"which":[51,115],"can":[52],"produce":[53],"much":[54],"more":[55],"successful":[56],"results":[57],"less":[59],"time":[60],"than":[61],"calculation":[63],"techniques.":[64],"Among":[65],"all":[66],"tools,":[69],"synthetic":[70],"aperture":[71],"radar":[72],"(SAR)":[73],"has":[74],"a":[75,95,194,213,231],"significant":[76],"impact":[77],"on":[78,94],"determining":[79,208],"terrains.":[83],"The":[84],"main":[85],"objective":[86],"study":[89],"predict":[92],"yearly":[96],"basis":[97],"vegetation-covered":[100],"aid":[104],"different":[106,138,145],"machine":[107,195],"learning":[108,196],"SAR":[111],"based":[112,197],"Radarsat-2":[113],"data,":[114],"obtained":[116,143,204],"2015":[118],"2016":[120],"years.The":[121],"proposed":[122],"system":[123,181],"consists":[124],"several":[126],"stages,":[127],"respectively.":[128],"feature":[131,198,205,210,214,224,240],"extraction":[132],"stage,":[133,167,193],"backscatter":[135],"coefficients":[136],"polarizations":[139],"parameters":[142,218],"from":[144],"models":[146],"decomposition":[148],"(Freeman-Durden":[149],"H/A/$\\alpha$)":[151],"were":[152,158,183],"combined":[153],"nine":[155],"polarimetric":[156],"features":[157],"formed":[159],"for":[160,185,207],"each":[161],"sample":[162],"point.":[163],"next":[166],"support":[168],"vector":[169],"regression":[170,173],"(SVR),":[171],"generalized":[172],"neural":[174],"network":[175],"(GRNN)":[176],"adaptive":[178],"neuro-fuzzy":[179],"inference":[180],"(ANFIS)":[182],"employed":[184],"prediction":[187,229],"SWC.":[189],"last":[192],"selection":[199],"was":[200,219,235],"implemented":[201],"vectors":[206],"optimal":[209,223],"sets.":[211],"Finally,":[212],"set":[215,225,241],"6":[217],"determined":[220],"as":[221],"most":[222],"slightly":[232],"better":[233],"performance":[234],"observed":[236],"thanks":[237],"compared":[242],"other":[245],"results.":[246]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
