{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T17:17:26Z","timestamp":1773163046255,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"This research has been funded by Scientific Research Deanship at University of Ha\u2019il-Saudi Arabia"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s00521-025-11026-7","type":"journal-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T15:37:26Z","timestamp":1739633846000},"page":"8773-8797","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Evidential uncertainty quantification with multiple deep learning architectures for spatiotemporal drought forecasting"],"prefix":"10.1007","volume":"37","author":[{"given":"Ahlem","family":"Ferchichi","sequence":"first","affiliation":[]},{"given":"Mejda","family":"Chihaoui","sequence":"additional","affiliation":[]},{"given":"Radhia","family":"Toujani","sequence":"additional","affiliation":[]},{"given":"Aya","family":"Ferchichi","sequence":"additional","affiliation":[]},{"given":"Fatma","family":"Hendaoui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,15]]},"reference":[{"issue":"9","key":"11026_CR1","doi-asserted-by":"publisher","first-page":"6048","DOI":"10.1109\/JSTARS.2023.3290685","volume":"16","author":"JY Seo","year":"2023","unstructured":"Seo JY, Lee S-I (2023) Probabilistic evaluation of drought propagation using satellite data and deep learning model: from precipitation to soil moisture and groundwater. J Sel Top Appl Earth Obs Remote Sens 16(9):6048\u20136061","journal-title":"J Sel Top Appl Earth Obs Remote Sens"},{"issue":"8","key":"11026_CR2","doi-asserted-by":"publisher","first-page":"122211","DOI":"10.1016\/j.eswa.2023.122211","volume":"238","author":"A Ferchichi","year":"2024","unstructured":"Ferchichi A, Chihaoui M, Ferchichi A (2024) Spatio-temporal modeling of climate change impacts on drought forecast using generative adversarial network:a case study in africa. Expert Syst Appl 238(8):122211","journal-title":"Expert Syst Appl"},{"issue":"4","key":"11026_CR3","doi-asserted-by":"publisher","first-page":"101552","DOI":"10.1016\/j.ecoinf.2022.101552","volume":"68","author":"A Ferchichi","year":"2022","unstructured":"Ferchichi A, Abbes AB, Barra V, Farah IR (2022) Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: a systematic literature review. Ecol Inform 68(4):101552","journal-title":"Ecol Inform"},{"issue":"3","key":"11026_CR4","first-page":"3179","volume":"31","author":"M Sensoy","year":"2018","unstructured":"Sensoy M, Kaplan L, Kandemir M (2018) Evidential deep learning to quantify classification uncertainty. Adv Neural Inf Process Syst 31(3):3179\u20133189","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1251","key":"11026_CR5","first-page":"14927","volume":"33","author":"A Amini","year":"2020","unstructured":"Amini A, Schwarting W, Soleimany A, Rus D (2020) Deep evidential regression. Adv Neural Inf Process Syst 33(1251):14927\u201314937","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1251","key":"11026_CR6","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s10115-017-1102-9","volume":"55","author":"A Ferchichi","year":"2018","unstructured":"Ferchichi A, Boulila W, Farah IR (2018) Reducing uncertainties in land cover change prediction models using sensitivity analysis. Knowl Inf Syst 55(1251):719\u2013740","journal-title":"Knowl Inf Syst"},{"issue":"1251","key":"11026_CR7","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.ecoinf.2016.11.006","volume":"37","author":"A Ferchichi","year":"2017","unstructured":"Ferchichi A, Boulila W, Farah IR (2017) Propagating aleatory and epistemic uncertainty in land cover change prediction process. Ecol Inform 37(1251):24\u201337","journal-title":"Ecol Inform"},{"issue":"6","key":"11026_CR8","doi-asserted-by":"publisher","first-page":"065012","DOI":"10.1088\/1748-9326\/ac7247","volume":"17","author":"SS Tabas","year":"2022","unstructured":"Tabas SS, Samadi V (2022) Variational bayesian dropout with a gaussian prior for recurrent neural networks application in rainfall-runoff modeling. Environ Res Lett 17(6):065012","journal-title":"Environ Res Lett"},{"key":"11026_CR9","doi-asserted-by":"crossref","unstructured":"Schreck JS, II DJG, Becker C, Chapman WE, Elmore K, Fan D, Gantos G, Kim E, Kimpara D, Martin T, Molina MJ, Pryzbylo VM, Radford J, Saavedra B, Willson J, Wirz C (2024) Evidential deep learning: enhancing predictive uncertainty estimation for earth system science applications","DOI":"10.1175\/AIES-D-23-0093.1"},{"key":"11026_CR10","doi-asserted-by":"crossref","unstructured":"Yuan B, Yue X, Lv Y, Denoeux T (2020) Evidential deep neural networks for uncertain data classification. In: knowledge science, engineering and management. Springer p 427\u2013437","DOI":"10.1007\/978-3-030-55393-7_38"},{"key":"11026_CR11","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ijar.2022.08.013","volume":"150","author":"S Xu","year":"2022","unstructured":"Xu S, Chen Y, Ma C, Yue X (2022) Deep evidential fusion network for medical image classification. Int J Approx Reason 150:188\u2013198","journal-title":"Int J Approx Reason"},{"key":"11026_CR12","doi-asserted-by":"crossref","unstructured":"Bao W, Yu Q, Kong Y (2021). Evidential deep learning for open set action recognition. In: 2021 IEEE\/CVF international conference on computer vision (ICCV), p 13329\u201313338","DOI":"10.1109\/ICCV48922.2021.01310"},{"key":"11026_CR13","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1214\/aoms\/1177698950","volume":"38","author":"AP Dempster","year":"1967","unstructured":"Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38:325\u2013339","journal-title":"Ann Math Stat"},{"key":"11026_CR14","doi-asserted-by":"crossref","unstructured":"Shafer G (1976) Mathematical theory of evidence. Princeton University Press","DOI":"10.1515\/9780691214696"},{"key":"11026_CR15","doi-asserted-by":"crossref","unstructured":"Tsiligkaridis T (2021) Failure prediction by confidence estimation of uncertainty-aware dirichlet networks. In: ICASSP 2021 - 2021 IEEE international conference on acoustics, speech and signal processing (ICASSP), p 3525\u20133529","DOI":"10.1109\/ICASSP39728.2021.9414153"},{"key":"11026_CR16","doi-asserted-by":"publisher","first-page":"22071","DOI":"10.1007\/s00521-022-08016-4","volume":"35","author":"H Li","year":"2023","unstructured":"Li H, Nan Y, Ser JD, Yang G (2023) Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation. Neural Comput Appl 35:22071\u201322085","journal-title":"Neural Comput Appl"},{"issue":"11","key":"11026_CR17","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.5194\/hess-26-2923-2022","volume":"26","author":"L Xu","year":"2022","unstructured":"Xu L, Chen N, Yang C, Yu H, Chen Z (2022) Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning. Hydrol Earth Syst Sci 26(11):2923\u20132938","journal-title":"Hydrol Earth Syst Sci"},{"key":"11026_CR18","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1007\/s13762-020-02862-2","volume":"18","author":"S Zhu","year":"2021","unstructured":"Zhu S, Xu Z, Luo X, Liu X, Wang R, Zhang M, Huo Z (2021) Internal and external coupling of Gaussian mixture model and deep recurrent network for probabilistic drought forecasting. Int J Environ Sci Technol 18:1221\u20131236","journal-title":"Int J Environ Sci Technol"},{"issue":"6","key":"11026_CR19","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1175\/MWR-D-22-0268.1","volume":"151","author":"W Hu","year":"2023","unstructured":"Hu W, Ghazvinian M, Chapman WE, Sengupta A, Ralph FM, Monache LD (2023) Deep learning forecast uncertainty for precipitation over the western united states. Monthly Weather Rev 151(6):1367\u20131385","journal-title":"Monthly Weather Rev"},{"key":"11026_CR20","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.5194\/hess-26-1673-2022","volume":"26","author":"D Klotz","year":"2022","unstructured":"Klotz D, Kratzert F, Gauch M, Sampson AK, Klambauer G, Hochreiter S, Nearing G (2022) Uncertainty estimation with deep learning for rainfall-runoff modeling. Hydrol Earth Syst Sci 26:1673\u20131693","journal-title":"Hydrol Earth Syst Sci"},{"issue":"5","key":"11026_CR21","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1175\/MWR-D-23-0097.1","volume":"152","author":"J Wang","year":"2024","unstructured":"Wang J, Wang X, Guan J, Zhang L, Chang T, Yu W (2024) St-transnet: a spatiotemporal transformer network for uncertainty estimation from a single deterministic precipitation forecast. Monthly Weather Rev 152(5):1163\u20131178","journal-title":"Monthly Weather Rev"},{"key":"11026_CR22","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1007\/s00477-022-02181-7","volume":"36","author":"V Nourani","year":"2022","unstructured":"Nourani V, Khodkar K, Paknezhad NJ, Laux P (2022) Deep learning-based uncertainty quantification of groundwater level predictions. Stoch Environ Res Risk Assess 36:3081\u20133107","journal-title":"Stoch Environ Res Risk Assess"},{"issue":"1251","key":"11026_CR23","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s40595-016-0088-7","volume":"4","author":"A Ferchichi","year":"2017","unstructured":"Ferchichi A, Boulila W, Farah IR (2017) Towards an uncertainty reduction framework for land-cover change prediction using possibility theory. Vietnam J Comput Sci 4(1251):195\u2013209","journal-title":"Vietnam J Comput Sci"},{"key":"11026_CR24","doi-asserted-by":"crossref","unstructured":"Ferchichi A, Boulila W, Farah IR (2017) Improvement of lcc prediction modeling based on correlated parameters and model structure uncertainty propagation. In: Computational intelligence. Springer p 270\u2013290","DOI":"10.1007\/978-3-319-48506-5_14"},{"key":"11026_CR25","doi-asserted-by":"crossref","unstructured":"Ferchichi A, Boulila W, Farah IR (2014) Parameter and structural model imperfection propagation using evidence theory in land cover change prediction. In: International image processing, applications and systems conference, vol 669. IEEE p 1\u20136","DOI":"10.1109\/IPAS.2014.7043270"},{"key":"11026_CR26","doi-asserted-by":"publisher","first-page":"118212","DOI":"10.1016\/j.eswa.2022.118212","volume":"209","author":"M Chehibi","year":"2022","unstructured":"Chehibi M, Ferchichi A, Farah IR (2022) Representing and modeling spatio-temporal uncertainty using belief function theory in flood extent mapping. Expert Syst Appl 209:118212","journal-title":"Expert Syst Appl"},{"issue":"1251","key":"11026_CR27","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.neucom.2021.03.066","volume":"450","author":"Z Tong","year":"2021","unstructured":"Tong Z, Xu P, Den\u0153ux T (2021) Deep evidential regressionan evidential classifier based on Dempster\u2013Shafer theory and deep learning. Neurocomputing 450(1251):275\u2013293","journal-title":"Neurocomputing"},{"key":"11026_CR28","unstructured":"Ryu JJ, Shen M, Ghosh S, Bu Y, Sattigeri P, Das S, Wornell GW (2024) Improved evidential deep learning via a mixture of dirichlet distributions. In: ArXiv, vol. 2402.06160"},{"issue":"2","key":"11026_CR29","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1007\/s10708-022-10733-1","volume":"88","author":"Y Bedasa","year":"2023","unstructured":"Bedasa Y, Bedemo A (2023) The effect of climate change on food insecurity in the horn of Africa. GeoJournal 88(2):1829\u20131839","journal-title":"GeoJournal"},{"key":"11026_CR30","unstructured":"Spinoni J, Naumann G, Vogt JV, Barbosa P (2016) Meteorological droughts in Europe: events and impacts-past trends and future projections. Publications Office of the European Union p 1\u201311"},{"key":"11026_CR31","doi-asserted-by":"publisher","first-page":"167","DOI":"10.5194\/npg-30-167-2023","volume":"30","author":"D Giaquinto","year":"2023","unstructured":"Giaquinto D, Marzocchi W, Kurths J (2023) Exploring meteorological droughts\u2019 spatial patterns across Europe through complex network theory. Nonlinear Process Geophys 30:167\u2013181","journal-title":"Nonlinear Process Geophys"},{"issue":"2","key":"11026_CR32","first-page":"328","volume":"8","author":"C Lucrezia","year":"2019","unstructured":"Lucrezia C, Mancinelli G, Scirocco T, Specchiulli A (2019) First record of sinanodonta woodiana (Lea, 1834) in an artificial reservoir in the Molise region, southeast Italy. BioInvasions Rec 8(2):328\u2013330","journal-title":"BioInvasions Rec"},{"key":"11026_CR33","doi-asserted-by":"crossref","unstructured":"Worku MA (2024) Spatiotemporal analysis of drought severity using spi and spei: case study of semi-arid borana area, southern ethiopia. Frontiers in Environmental Science","DOI":"10.3389\/fenvs.2024.1337190"},{"key":"11026_CR34","doi-asserted-by":"crossref","unstructured":"Sardar VS, MYK, Chaudhari SS, Ghosh P (2021) Convolution neural network-based agriculture drought prediction using satellite images. In: 2021 IEEE Mysore sub section international conference (MysuruCon), p 601\u2013607","DOI":"10.1109\/MysuruCon52639.2021.9641531"},{"key":"11026_CR35","doi-asserted-by":"crossref","unstructured":"Chen Z, Wang G, Wei X, Liu Y, Duan Z, Hu Y, Jiang H (2024) Basin-scale daily drought prediction using convolutional neural networks in Fenhe river basin, China. Atmosphere 15(155)","DOI":"10.3390\/atmos15020155"},{"issue":"155","key":"11026_CR36","doi-asserted-by":"publisher","first-page":"166361","DOI":"10.1016\/j.scitotenv.2023.166361","volume":"902","author":"T Wang","year":"2023","unstructured":"Wang T, Tu X, Singh VP, Chen X, Zhou KLZ (2023) Drought prediction: insights from the fusion of lstm and multi-source factors. Sci Total Environ 902(155):166361","journal-title":"Sci Total Environ"},{"key":"11026_CR37","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1007\/s00477-023-02465-6","volume":"37","author":"Y Kheyruri","year":"2023","unstructured":"Kheyruri Y, Sharafati A, Neshat A (2023) Predicting agricultural drought using meteorological and enso parameters in different regions of Iran based on the lstm model. Stoch Environ Res Risk Assess 37:3599\u20133613","journal-title":"Stoch Environ Res Risk Assess"},{"key":"11026_CR38","doi-asserted-by":"publisher","first-page":"107563","DOI":"10.1016\/j.engappai.2023.107563","volume":"128","author":"A Ferchichi","year":"2024","unstructured":"Ferchichi A, Abbes AB, Barra V, Rhif M, Farah IR (2024) Multi-attention generative adversarial network for multi-step vegetation indices forecasting using multivariate time series. Eng Appl Artif Intell 128:107563","journal-title":"Eng Appl Artif Intell"},{"key":"11026_CR39","doi-asserted-by":"crossref","unstructured":"Foroumandi E, Gavahi K, Hamid M (2024) Generative adversarial network for real-time flash drought monitoring: a deep learning study. Water Resour Res 60(5)","DOI":"10.1029\/2023WR035600"},{"key":"11026_CR40","doi-asserted-by":"crossref","unstructured":"Nghiem T-L, Le V-D, Le T-L, Mar\u00e9chal P, Delahaye D, Vidosavljevic A (2022) Applying Bayesian inference in a hybrid cnn-lstm model for time-series prediction. In: 2022 international conference on multimedia analysis and pattern recognition (MAPR), p 1\u20136","DOI":"10.1109\/MAPR56351.2022.9924783"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11026-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11026-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11026-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T08:48:38Z","timestamp":1746434918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11026-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,15]]},"references-count":40,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["11026"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11026-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,15]]},"assertion":[{"value":"18 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2025","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 they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest:"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}