{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T20:30:06Z","timestamp":1772569806989,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Brain Korea 21 Program for Leading Uni-versities","award":["BK21 FOUR"],"award-info":[{"award-number":["BK21 FOUR"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s12145-024-01386-4","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T10:01:37Z","timestamp":1721383297000},"page":"4639-4653","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Digital mapping of coastal landscapes integrating ocean-environment relationships and machine learning"],"prefix":"10.1007","volume":"17","author":[{"given":"Kui","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"issue":"11","key":"1386_CR15","doi-asserted-by":"publisher","first-page":"13736","DOI":"10.1007\/s11356-020-11158-4","volume":"28","author":"HM Al-Mayahi","year":"2021","unstructured":"Al-Mayahi HM, Al-Abadi AM, Fryar AE (2021) Probability mapping of groundwater contamination by hydrocarbon from the deep oil reservoirs using gis-based machine-learning algorithms: a case study of the Dammam aquifer (middle of Iraq). Environ Sci Pollut Res 28(11):13736\u201313751","journal-title":"Environ Sci Pollut Res"},{"issue":"3","key":"1386_CR30","doi-asserted-by":"publisher","first-page":"446","DOI":"10.3390\/rs16030446","volume":"16","author":"RM Cavalli","year":"2024","unstructured":"Cavalli RM (2024) Remote data for Mapping and Monitoring Coastal Phenomena and parameters: a systematic review. Remote Sens 16(3):446","journal-title":"Remote Sens"},{"key":"1386_CR23","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1016\/j.scitotenv.2019.03.151","volume":"669","author":"D Chen","year":"2019","unstructured":"Chen D, Chang N, Xiao J, Zhou Q, Wu W (2019) Mapping dynamics of soil organic matter in croplands with modis data and machine learning algorithms. ence Total Environ 669:844\u2013855","journal-title":"ence Total Environ"},{"issue":"6","key":"1386_CR17","doi-asserted-by":"publisher","first-page":"3584","DOI":"10.1364\/BOE.421333","volume":"12","author":"X Chen","year":"2021","unstructured":"Chen X, Zhang Y, Li X, Yang Z, Liu A, Yu X (2021) Diagnosis and staging of multiple myeloma using serum-based laser-induced breakdown spectroscopy combined with machine learning methods. Biomedical Opt Express 12(6):3584\u20133596","journal-title":"Biomedical Opt Express"},{"issue":"2","key":"1386_CR6","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s42974-022-00077-8","volume":"23","author":"P Chen","year":"2022","unstructured":"Chen P, Zhao C, Duan D, Wang F (2022) Extracting tea plantations in complex landscapes using sentinel-2 imagery and machine learning algorithms. Community Ecol 23(2):163\u2013172","journal-title":"Community Ecol"},{"key":"1386_CR29","doi-asserted-by":"crossref","unstructured":"Danoedoro P, Widayani P, Hidayati IN, Kartika CSD, Alfani F (2024) Incorporating landscape ecological approach in machine learning classification for agricultural land-use mapping based on a single date imagery. Geocarto Int, 39(1)","DOI":"10.1080\/10106049.2024.2356844"},{"key":"1386_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/JEDS.2024.3358087","author":"PM Dr","year":"2024","unstructured":"Kumat PM et al (2024) Wind and Solar Energy Contact with Clean Environment Enrichment. IEEE J Electron Devices Soc. https:\/\/doi.org\/10.1109\/JEDS.2024.3358087","journal-title":"IEEE J Electron Devices Soc"},{"issue":"8","key":"1386_CR20","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1080\/09537325.2020.1732912","volume":"32","author":"ESM El-Alfy","year":"2020","unstructured":"El-Alfy ESM, Mohammed SA (2020) A review of machine learning for big data analytics: bibliometric approach. Technol Anal Strateg Manag 32(8):984\u20131005","journal-title":"Technol Anal Strateg Manag"},{"key":"1386_CR2","doi-asserted-by":"crossref","unstructured":"Fan C, Wu F, Mostafavi A (2020). A hybrid machine learning pipeline for automated mapping of events and locations from social media in disasters. IEEE Access, PP(99), 1\u20131","DOI":"10.1109\/ACCESS.2020.2965550"},{"key":"1386_CR19","unstructured":"Golmohammadi D, Parast MM, Sanders N (2020). The impact of service failures on firm profitability: integrating machine learning and statistical modeling. IEEE Trans Eng Manage, PP(99), 1\u201315"},{"issue":"5","key":"1386_CR16","doi-asserted-by":"publisher","first-page":"6926","DOI":"10.3934\/mbe.2021344","volume":"18","author":"X Gu","year":"2021","unstructured":"Gu X, Yang B, Gao S, Yan L, Xu D, Wang W (2021) Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning. Math Biosci Eng 18(5):6926\u20136940","journal-title":"Math Biosci Eng"},{"issue":"5","key":"1386_CR10","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1080\/15265161.2022.2059206","volume":"22","author":"K Jaffe","year":"2022","unstructured":"Jaffe K, Moreno J, Rahimzadeh V, Spector-Bagdady K (2022) Promoting ethical deployment of artificial intelligence and machine learning in healthcare. Am J Bioeth 22(5):4\u20137","journal-title":"Am J Bioeth"},{"issue":"3","key":"1386_CR11","first-page":"817","volume":"25","author":"G Kaur","year":"2022","unstructured":"Kaur G, Joshi S, Rao K, Tiwari A, Sharma A, Jain H et al (2022) Machine learning approach for phishing website detection : a literature survey. J Discrete Math Sci Crypt 25(3):817\u2013827","journal-title":"J Discrete Math Sci Crypt"},{"issue":"May","key":"1386_CR1","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.apm.2019.12.016","volume":"81","author":"Y Khaledian","year":"2020","unstructured":"Khaledian Y, Miller BA (2020) Selecting appropriate machine learning methods for digital soil mapping. Appl Math Model 81(May):401\u2013418","journal-title":"Appl Math Model"},{"issue":"3","key":"1386_CR13","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1080\/01431161.2021.2020364","volume":"43","author":"LM Koerner","year":"2022","unstructured":"Koerner LM, Chadwick MA, Tebbs EJ (2022) Mapping invasive strawberry guava (psidium cattleianum) in tropical forests of Mauritius with sentinel-2 and machine learning. Int J Remote Sens 43(3):841\u2013872","journal-title":"Int J Remote Sens"},{"issue":"6","key":"1386_CR22","doi-asserted-by":"publisher","first-page":"514","DOI":"10.3390\/rs8060514","volume":"8","author":"X Li","year":"2018","unstructured":"Li X, Chen W, Cheng X, Wang L (2018) Remote sensing a comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ziyuan-3 stereo satellite imagery. Remote Sens 8(6):514","journal-title":"Remote Sens"},{"issue":"3","key":"1386_CR8","doi-asserted-by":"publisher","first-page":"101819","DOI":"10.1016\/j.eti.2021.101819","volume":"24","author":"J Luo","year":"2021","unstructured":"Luo J (2021) Online design of green urban garden landscape based on machine learning and computer simulation technology. Environ Technol Innov 24(3):101819","journal-title":"Environ Technol Innov"},{"issue":"19","key":"1386_CR5","first-page":"124","volume":"36","author":"G Ma","year":"2020","unstructured":"Ma G, Ding J, Han L, Zhang Z (2020) Digital mapping of soil salinization in arid area wetland based on variable optimized selection and machine learning. Nongye Gongcheng Xuebao\/Transactions Chin Soc Agricultural Eng 36(19):124\u2013131","journal-title":"Nongye Gongcheng Xuebao\/Transactions Chin Soc Agricultural Eng"},{"issue":"5","key":"1386_CR12","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1080\/15265161.2022.2055212","volume":"22","author":"N Martinez-Martin","year":"2022","unstructured":"Martinez-Martin N, Cho MK (2022) Bridging the Ai chasm: can ebm address representation and fairness in clinical machine learning? Am J Bioeth 22(5):30\u201332","journal-title":"Am J Bioeth"},{"key":"1386_CR21","doi-asserted-by":"publisher","first-page":"25521","DOI":"10.1109\/ACCESS.2020.2969728","volume":"8","author":"SV Oprea","year":"2020","unstructured":"Oprea SV, Bra A (2020) Setting the time-of-use tariff rates with nosql and machine learning to a sustainable environment. IEEE Access 8:25521\u201325530","journal-title":"IEEE Access"},{"issue":"S2","key":"1386_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1017\/S1431927621013180","volume":"27","author":"C Qian","year":"2021","unstructured":"Qian C, Yao L, Liu C, Smith JW, Chen Q (2021) Integrating machine learning with liquid-phase tem imaging to study nanoscale crystallization and macromolecular heterogeneity. Microsc Microanal 27(S2):37\u201338","journal-title":"Microsc Microanal"},{"key":"1386_CR28","doi-asserted-by":"crossref","unstructured":"Rajmohan G, Chinnappan CV, William J, Balakrishnan ADC, Muthu SA, B., Manogaran G (2021) Revamping land coverage analysis using aerial satellite image mapping. Trans Emerg Telecommunications Technol, 32(7), e3927","DOI":"10.1002\/ett.3927"},{"key":"1386_CR27","doi-asserted-by":"publisher","DOI":"10.1142\/S0218843024500126","author":"P Sathyaprakash","year":"2023","unstructured":"Sathyaprakash P et al (2023) Medical practitioner-centric heterogeneous network powered efficient E-Healthcare risk prediction on Health Big Data. Int J Coop Inform Syst. https:\/\/doi.org\/10.1142\/S0218843024500126","journal-title":"Int J Coop Inform Syst"},{"key":"1386_CR99","unstructured":"Schaffer JD, Caruana R, Eshelman LJ, Das R (1989, June). A study of control parameters affecting online performance of genetic algorithms for function optimization. In Proceedings of the 3rd international conference on genetic algorithms (pp. 51-60)."},{"issue":"2","key":"1386_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12665-021-10147-1","volume":"81","author":"SS Singha","year":"2022","unstructured":"Singha SS, Singha S, Pasupuleti S, Venkatesh AS (2022) Knowledge-driven and machine learning decision tree-based approach for assessment of geospatial variation of groundwater quality around coal mining regions, Korba district, central India. Environ Earth Sci 81(2):1\u201313","journal-title":"Environ Earth Sci"},{"issue":"2","key":"1386_CR7","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1007\/s40808-021-01175-8","volume":"8","author":"H Tamiru","year":"2022","unstructured":"Tamiru H, Wagari M (2022) Machine-learning and hec-ras integrated models for flood inundation mapping in baro river basin, Ethiopia. Model Earth Syst Environ 8(2):2291\u20132303","journal-title":"Model Earth Syst Environ"},{"issue":"4","key":"1386_CR25","first-page":"291","volume":"8","author":"H Tanaka","year":"2019","unstructured":"Tanaka H, Matsuoka Y, Kawakami T, Azegami Y, Yamamoto M, Ohtake K et al (2019) Optimization calculations and machine learning aimed at reduction of wind forces acting on tall buildings and mitigation of wind environment. Int J High-Rise Build 8(4):291\u2013302","journal-title":"Int J High-Rise Build"},{"issue":"7","key":"1386_CR24","doi-asserted-by":"publisher","first-page":"4785","DOI":"10.1029\/2017WR021749","volume":"54","author":"B Wang","year":"2018","unstructured":"Wang B, Hipsey MR, Ahmed S, Oldham C (2018) The impact of landscape characteristics on groundwater dissolved organic nitrogen: insights from machine learning methods and sensitivity analysis. Water Resour Res 54(7):4785\u20134804","journal-title":"Water Resour Res"},{"issue":"9","key":"1386_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10064-022-02874-x","volume":"81","author":"AM Youssef","year":"2022","unstructured":"Youssef AM, Mahdi AM, Pourghasemi HR (2022) Landslides and flood multi-hazard assessment using machine learning techniques. Bull Eng Geol Environ 81(9):1\u201323","journal-title":"Bull Eng Geol Environ"},{"key":"1386_CR18","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.neunet.2021.09.003","volume":"144","author":"J Zhang","year":"2021","unstructured":"Zhang J, Li Z, Song X, Ning H (2021) Deep tobit networks: a novel machine learning approach to microeconometrics. Neural Netw 144:279\u2013296","journal-title":"Neural Netw"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01386-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01386-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01386-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T18:22:00Z","timestamp":1729102920000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01386-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,19]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1386"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01386-4","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,19]]},"assertion":[{"value":"23 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}