{"id":"https://openalex.org/W2977881416","doi":"https://doi.org/10.1109/ijcnn.2019.8852229","title":"A Deep Learning based architecture for rainfall estimation integrating heterogeneous data sources","display_name":"A Deep Learning based architecture for rainfall estimation integrating heterogeneous data sources","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977881416","doi":"https://doi.org/10.1109/ijcnn.2019.8852229","mag":"2977881416"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5013173651","display_name":"Gianluigi Folino","orcid":"https://orcid.org/0000-0002-8139-3445"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gianluigi Folino","raw_affiliation_strings":["ICAR-CNR, Italy"],"affiliations":[{"raw_affiliation_string":"ICAR-CNR, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025844977","display_name":"Massimo Guarascio","orcid":"https://orcid.org/0000-0001-7711-9833"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Massimo Guarascio","raw_affiliation_strings":["ICAR-CNR, Italy"],"affiliations":[{"raw_affiliation_string":"ICAR-CNR, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003618685","display_name":"Francesco Chiaravalloti","orcid":"https://orcid.org/0000-0002-9394-5550"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Francesco Chiaravalloti","raw_affiliation_strings":["ICAR-CNR, Italy"],"affiliations":[{"raw_affiliation_string":"ICAR-CNR, Italy","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073102176","display_name":"Salvatore Gabriele","orcid":"https://orcid.org/0000-0001-9319-5051"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salvatore Gabriele","raw_affiliation_strings":["ICAR-CNR, Italy"],"affiliations":[{"raw_affiliation_string":"ICAR-CNR, Italy","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5013173651"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7349,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.71545926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9940999746322632,"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":0.7274786233901978},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6622544527053833},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5472665429115295},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5074881911277771},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4976356327533722},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.44719409942626953},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3558999300003052},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.35203081369400024},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.18388280272483826},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.1624622941017151},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08100420236587524},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07813414931297302}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7274786233901978},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6622544527053833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5472665429115295},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5074881911277771},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4976356327533722},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.44719409942626953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3558999300003052},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35203081369400024},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.18388280272483826},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.1624622941017151},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08100420236587524},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07813414931297302},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1550206690","https://openalex.org/W1665214252","https://openalex.org/W1836465849","https://openalex.org/W1973475129","https://openalex.org/W2012159836","https://openalex.org/W2035802397","https://openalex.org/W2055308682","https://openalex.org/W2095705004","https://openalex.org/W2112362638","https://openalex.org/W2142854301","https://openalex.org/W2162789299","https://openalex.org/W2166452583","https://openalex.org/W2170505850","https://openalex.org/W2177044256","https://openalex.org/W2264487822","https://openalex.org/W2275281333","https://openalex.org/W2340938351","https://openalex.org/W2559001317","https://openalex.org/W2735245265","https://openalex.org/W2897265228","https://openalex.org/W6633142322","https://openalex.org/W6637242042","https://openalex.org/W6638667902"],"related_works":["https://openalex.org/W4312200629","https://openalex.org/W4223943233","https://openalex.org/W4309045103","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W4299487748","https://openalex.org/W3164822677","https://openalex.org/W4250304930","https://openalex.org/W3215138031","https://openalex.org/W2922457425"],"abstract_inverted_index":{"Rain":[0],"gauges":[1],"are":[2,19],"sensors":[3],"providing":[4],"direct":[5],"measurement":[6],"of":[7,25,33,113,142],"precipitation":[8,27],"intensity":[9],"at":[10],"individual":[11],"point":[12],"sites,":[13],"and,":[14],"usually,":[15],"spatial":[16],"interpolation":[17,53],"methods":[18,54],"used":[20,45,66],"to":[21,56,109],"obtain":[22],"an":[23],"estimate":[24],"the":[26,30,114,117,140],"field":[28],"over":[29],"entire":[31],"area":[32],"interest.":[34],"Among":[35],"them,":[36],"Kriging":[37],"with":[38,58,151],"External":[39],"Drift":[40],"(KED)":[41],"is":[42,124],"a":[43,76,88,133],"largely":[44],"and":[46,61,104,153],"well-recognized":[47],"method":[48],"in":[49,67,116,136,149],"this":[50,72],"field.":[51],"However,":[52],"need":[55],"work":[57],"real-time":[59,68],"data,":[60],"therefore":[62],"can":[63,82],"be":[64,83],"hardly":[65],"scenarios.":[69],"To":[70],"overcome":[71],"issue,":[73],"we":[74],"propose":[75],"general":[77],"machine":[78,155],"learning":[79,90,156],"framework,":[80],"which":[81],"trained":[84],"offline,":[85],"based":[86],"on":[87,129],"deep":[89],"architecture,":[91],"also":[92],"integrating":[93],"information":[94],"derived":[95],"from":[96],"remote":[97],"sensing":[98],"measurements":[99],"such":[100],"as":[101],"weather":[102],"radars":[103],"satellites.":[105],"The":[106],"framework":[107],"allows":[108],"provide":[110],"accurate":[111],"estimations":[112],"rainfall":[115],"areas":[118],"where":[119],"no":[120],"rain":[121],"gauge":[122],"data":[123,131],"available.":[125],"Experimental":[126],"results,":[127],"conducted":[128],"real":[130],"concerning":[132],"southern":[134],"region":[135],"Italy,":[137],"provided":[138],"by":[139],"Department":[141],"Civil":[143],"Protection":[144],"(DCP),":[145],"show":[146],"significant":[147],"improvement":[148],"comparison":[150],"KED":[152],"other":[154],"techniques.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}