{"id":"https://openalex.org/W4389633775","doi":"https://doi.org/10.1109/access.2023.3341587","title":"Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches","display_name":"Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389633775","doi":"https://doi.org/10.1109/access.2023.3341587"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3341587","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3341587","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10355928.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10355928.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090684997","display_name":"Geetabai S Hukkeri","orcid":"https://orcid.org/0000-0001-9511-8578"},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Geetabai S. Hukkeri","raw_affiliation_strings":["Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"raw_orcid":"https://orcid.org/0000-0001-9511-8578","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089194470","display_name":"Sujay Raghavendra Naganna","orcid":"https://orcid.org/0000-0002-0482-1936"},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sujay Raghavendra Naganna","raw_affiliation_strings":["Department of Civil Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"raw_orcid":"https://orcid.org/0000-0002-0482-1936","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086572952","display_name":"P. Dayananda","orcid":"https://orcid.org/0000-0001-8445-3469"},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dayananda Pruthviraja","raw_affiliation_strings":["Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"raw_orcid":"https://orcid.org/0000-0001-8445-3469","affiliations":[{"raw_affiliation_string":"Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081566637","display_name":"Nagaraj Bhat","orcid":"https://orcid.org/0000-0002-0217-6718"},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]},{"id":"https://openalex.org/I4210145362","display_name":"Institute of Technology Management","ror":"https://ror.org/03c4qaa56","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1340206300","https://openalex.org/I4210145362","https://openalex.org/I4210150591"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nagaraj Bhat","raw_affiliation_strings":["Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal, Udupi, India","Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal, Udupi, India","institution_ids":["https://openalex.org/I4210145362"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070889906","display_name":"R. H. Goudar","orcid":"https://orcid.org/0000-0002-4590-7744"},"institutions":[{"id":"https://openalex.org/I65674248","display_name":"Visvesvaraya Technological University","ror":"https://ror.org/00ha14p11","country_code":"IN","type":"education","lineage":["https://openalex.org/I65674248"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"R. H. Goudar","raw_affiliation_strings":["Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi, Karnataka, India"],"raw_orcid":"https://orcid.org/0000-0002-4590-7744","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi, Karnataka, India","institution_ids":["https://openalex.org/I65674248"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.9393,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.85784428,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"141375","last_page":"141393"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11186","display_name":"Hydrology and Drought Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T11186","display_name":"Hydrology and Drought Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T10029","display_name":"Climate variability and models","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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 Forecasting Using AI","score":0.9836000204086304,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6082857251167297},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6069381237030029},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5333053469657898},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5288399457931519},{"id":"https://openalex.org/keywords/multivariate-adaptive-regression-splines","display_name":"Multivariate adaptive regression splines","score":0.5145191550254822},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5043195486068726},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.487576425075531},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.44766780734062195},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.4434784948825836},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4373798966407776},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43067434430122375},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.42112842202186584},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.36095112562179565},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.34656399488449097},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24388787150382996},{"id":"https://openalex.org/keywords/bayesian-multivariate-linear-regression","display_name":"Bayesian multivariate linear regression","score":0.228926420211792},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.22242671251296997},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15729087591171265}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6082857251167297},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6069381237030029},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5333053469657898},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5288399457931519},{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.5145191550254822},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5043195486068726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.487576425075531},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.44766780734062195},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.4434784948825836},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4373798966407776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43067434430122375},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.42112842202186584},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.36095112562179565},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.34656399488449097},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24388787150382996},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.228926420211792},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22242671251296997},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15729087591171265},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3341587","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3341587","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10355928.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c0caeccdb260459c940bbf6ad3a969db","is_oa":true,"landing_page_url":"https://doaj.org/article/c0caeccdb260459c940bbf6ad3a969db","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 141375-141393 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3341587","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3341587","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10355928.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320318872","display_name":"Manipal Academy of Higher Education","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389633775.pdf","grobid_xml":"https://content.openalex.org/works/W4389633775.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W210228696","https://openalex.org/W1548151805","https://openalex.org/W1678356000","https://openalex.org/W1733387413","https://openalex.org/W1847031783","https://openalex.org/W1964386488","https://openalex.org/W1985479415","https://openalex.org/W1987223597","https://openalex.org/W2027553546","https://openalex.org/W2044337727","https://openalex.org/W2054359607","https://openalex.org/W2068738735","https://openalex.org/W2079436405","https://openalex.org/W2088794999","https://openalex.org/W2101936368","https://openalex.org/W2102201073","https://openalex.org/W2104157175","https://openalex.org/W2115040356","https://openalex.org/W2171932819","https://openalex.org/W2391879671","https://openalex.org/W2612690371","https://openalex.org/W2801134799","https://openalex.org/W2898962279","https://openalex.org/W2937854818","https://openalex.org/W2973059396","https://openalex.org/W2983317741","https://openalex.org/W2990138404","https://openalex.org/W3025324289","https://openalex.org/W3094948551","https://openalex.org/W3191169055","https://openalex.org/W4206145899","https://openalex.org/W4212766645","https://openalex.org/W4235005132","https://openalex.org/W4235929704","https://openalex.org/W4250752670","https://openalex.org/W4252951897","https://openalex.org/W4281568839","https://openalex.org/W4288877484","https://openalex.org/W4289278167","https://openalex.org/W4293104113","https://openalex.org/W4293157199","https://openalex.org/W4296928277","https://openalex.org/W4297970760","https://openalex.org/W4307111613","https://openalex.org/W4320917642","https://openalex.org/W4322504591","https://openalex.org/W4362559509","https://openalex.org/W4378083171","https://openalex.org/W4385358529","https://openalex.org/W4385732903","https://openalex.org/W4402086095","https://openalex.org/W6683063705","https://openalex.org/W6745609711","https://openalex.org/W6750729320"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W4256152544","https://openalex.org/W31566076","https://openalex.org/W10576317","https://openalex.org/W1481829876","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W3172437324","https://openalex.org/W4200409524","https://openalex.org/W4366992530"],"abstract_inverted_index":{"Depending":[0],"on":[1,42,77],"the":[2,32,69,72,89,107,115,133,149,173,201,223,250,278,283],"severity":[3],"and":[4,21,25,66,172,180,192,209,218,244,261],"spatial-temporal":[5],"variability,":[6],"droughts":[7],"can":[8],"have":[9],"a":[10,286],"wide":[11],"range":[12],"of":[13,34,57,71,82,88,119,200,249,273],"impacts":[14],"such":[15,38],"as":[16,39,95,139,285],"crop":[17],"failure,":[18],"water":[19],"shortages,":[20],"food":[22],"insecurity.":[23],"Accurate":[24],"timely":[26],"forecasting":[27,81],"is":[28],"necessary":[29],"to":[30,113,147,187],"mitigate":[31],"hazards":[33],"extreme":[35],"weather":[36],"events,":[37],"droughts,":[40],"brought":[41],"by":[43],"climate":[44],"change.":[45],"A":[46],"district":[47,284],"like":[48],"Chitradurga":[49],"in":[50,290],"India,":[51],"which":[52,228],"typically":[53],"receives":[54],"around":[55],"450-600mm":[56],"annual":[58],"rainfall,":[59],"will":[60],"require":[61],"advanced":[62,163],"drought":[63,84,155,189,237,289],"mitigation":[64],"strategies":[65],"plans":[67],"before":[68],"onset":[70],"drought.":[73],"This":[74],"research":[75],"focuses":[76],"1-step":[78,124],"lead":[79,125],"time":[80,126,128,136],"meteorological":[83],"episodes":[85],"making":[86],"use":[87],"6-month":[90,116],"Standardised":[91],"Precipitation":[92],"Index":[93],"(SPI-6)":[94],"indicator.":[96],"The":[97,123,142,157,204,239,264],"fine":[98],"resolution":[99],"rainfall":[100],"data":[101,118,138],"(0.25\u00b0":[102],"\u00d7":[103],"0.25\u00b0)":[104],"obtained":[105],"from":[106,276],"Indian":[108],"Meteorological":[109],"Department":[110],"was":[111,130,145],"used":[112,146],"derive":[114],"SPI":[117],"23":[120],"grid":[121],"stations.":[122],"SPI-6":[127,135],"series":[129,137],"forecast":[131,188],"considering":[132],"antecedent":[134],"model":[140,255,274],"input.":[141],"Mutual":[143],"Information":[144],"determine":[148],"most":[150],"relevant":[151],"input":[152],"features":[153],"for":[154,197,236],"forecasting.":[156,238],"standard":[158],"Artificial":[159],"Neural":[160],"Network,":[161],"an":[162],"machine":[164],"learning":[165],"framework":[166],"-":[167],"Multivariate":[168],"Adaptive":[169],"Regression":[170,179],"Splines,":[171],"ensemble":[174],"learning-based":[175],"<italic":[176],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[177],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">CatBoost</i>":[178],"Gradient":[181],"Tree":[182],"Boosting":[183],"paradigms":[184],"were":[185,195,259,268],"employed":[186,196],"episodes.":[190],"Error":[191],"efficiency":[193,247],"metrics":[194],"performance":[198],"evaluation":[199],"simulated":[202],"models.":[203],"multivariate":[205,251],"adaptive":[206,252],"regression":[207,253],"splines":[208,254],"gradient":[210],"tree":[211],"boosting":[212],"forecasts":[213,275],"had":[214],"slightly":[215],"higher":[216],"accuracy":[217],"lower":[219],"error":[220,243],"rates":[221],"than":[222],"artificial":[224],"neural":[225],"network":[226],"model,":[227],"suggests":[229],"that":[230,267,282],"they":[231],"may":[232],"be":[233],"more":[234],"reliable":[235],"root":[240],"mean":[241],"square":[242],"normalized":[245],"Nash-Sutcliffe":[246],"ranges":[248],"(during":[256],"test":[257],"phase)":[258],"0.37\u20130.54":[260],"0.78\u20130.87,":[262],"respectively.":[263],"thematic":[265],"maps":[266],"created":[269],"using":[270],"spatial":[271],"interpolation":[272],"all":[277],"stations":[279],"also":[280],"confirmed":[281],"whole":[287],"experienced":[288],"April":[291],"2019.":[292]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}