{"authors":[{"id":"orcid_______::775e0a61603c0f4df230d64723739580","fullName":"Alejandro Cisterna-García","name":null,"surname":null,"rank":1,"pid":{"id":{"scheme":"orcid","value":"0000-0002-9927-7178"},"provenance":null}},{"id":null,"fullName":"Aurora González-Vidal","name":null,"surname":null,"rank":2,"pid":null},{"id":"orcid_______::d22990cfab40eb73f65e4668b823efca","fullName":"Antonio Martínez-Ibarra","name":null,"surname":null,"rank":3,"pid":{"id":{"scheme":"orcid","value":"0000-0001-6116-6811"},"provenance":null}},{"id":null,"fullName":"Yu Ye","name":null,"surname":null,"rank":4,"pid":null},{"id":"orcid_______::7d302e8f3d9f9062280eab5ac2168840","fullName":"Antonio Guillén-Teruel","name":null,"surname":null,"rank":5,"pid":{"id":{"scheme":"orcid","value":"0000-0002-7118-5335"},"provenance":null}},{"id":null,"fullName":"Luis Bernal-Escobedo","name":null,"surname":null,"rank":6,"pid":null},{"id":null,"fullName":"Antonio F. Skarmeta","name":"Antonio F.","surname":"Skarmeta","rank":7,"pid":null}],"openAccessColor":"hybrid","publiclyFunded":false,"eoscIfGuidelines":null,"type":"publication","language":{"code":"eng","label":"English"},"countries":null,"subjects":[{"subject":{"scheme":"keyword","value":"Science"},"provenance":null},{"subject":{"scheme":"keyword","value":"Q"},"provenance":null},{"subject":{"scheme":"FOS","value":"0207 environmental engineering"},"provenance":null},{"subject":{"scheme":"keyword","value":"R"},"provenance":null},{"subject":{"scheme":"keyword","value":"Medicine"},"provenance":null},{"subject":{"scheme":"FOS","value":"02 engineering and technology"},"provenance":null},{"subject":{"scheme":"keyword","value":"Article"},"provenance":null}],"mainTitle":"Artificial intelligence for streamflow prediction in river basins: a use case in Mar Menor","subTitle":null,"descriptions":["Streamflow prediction is crucial for efficient water resource management, flood forecasting and environmental protection. This is even more important in areas particularly vulnerable to environmental changes such our study area-the Mar Menor basin in the Region of Murcia, Spain-with a specific emphasis on the Albujón watercourse, a significant contributor to the Mar Menor. Utilizing data from stream gauge stations, nearby rain gauge stations, and piezometers, our research forecasts streamflow at two critical points: \"La Puebla\" and \"Desembocadura\" along the watercourse. Targeting short-term forecasts of 1, 12, and 24 hours, our study employs Machine and Deep Learning techniques after data preprocessing, which includes station selection, data granularity adjustment, and feature selection. A state-of-the-art data augmentation technique was used to balance periods of low and high streamflow. Results show that Random Forest slightly outperforms LSTM for 1-hour forecasts (NSE > 0.89, MAE < 0.01), while Long Short Term Memory with data augmentation excels for 12 and 24-hour forecasts (NSE > 0.12, MAE < 0.05). This is noteworthy in areas with torrential rains causing rapid streamflow increases, a more challenging yet less studied scenario in forecasting. The findings contribute to addressing the challenges associated with streamflow prediction in vulnerable regions."],"publicationDate":"2025-06-03","publisher":"Springer Science and Business Media LLC","embargoEndDate":null,"sources":["Crossref","Sci Rep","Scientific Reports, Vol 15, Iss 1, Pp 1-18 (2025)"],"formats":null,"contributors":null,"coverages":null,"bestAccessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/"},"container":{"name":"Scientific 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