{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:45:32Z","timestamp":1776159932761,"version":"3.50.1"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFC3210800"],"award-info":[{"award-number":["2024YFC3210800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132098","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:25:24Z","timestamp":1773854724000},"page":"132098","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Incremental learning\u2013based Kolmogorov\u2013Arnold Networks for adaptive hydrological parameter optimization of flood forecasting"],"prefix":"10.1016","volume":"319","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9177-0227","authenticated-orcid":false,"given":"Xin","family":"Chi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2627-5403","authenticated-orcid":false,"given":"Jun","family":"Feng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0643-0736","authenticated-orcid":false,"given":"Jiamin","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Pingping","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Jiru","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ronghao","family":"Yan","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.eswa.2026.132098_b0005","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s40069-025-00815-y","article-title":"Optimizing pozzolanic concrete mixtures using machine learning and global sensitivity analysis techniques","volume":"19","author":"Abdelsattar","year":"2025","journal-title":"International Journal of Concrete Structures and Materials"},{"key":"10.1016\/j.eswa.2026.132098_b0010","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., & Tuytelaars, T. (2018). Memory aware synapses: Learning what (not) to forget. In Proceedings of the European conference on computer vision (ECCV) (pp. 139\u2013154). https:\/\/arxiv.org\/abs\/1711.09601.","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"10.1016\/j.eswa.2026.132098_b0015","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Chakravarty, P., & Tuytelaars, T. (2017). Expert gate: Lifelong learning with a network of experts. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 3366\u20133375). https:\/\/doi.org\/10.1109\/CVPR.2017.753.","DOI":"10.1109\/CVPR.2017.753"},{"issue":"8","key":"10.1016\/j.eswa.2026.132098_b0020","first-page":"1","article-title":"Modeling tensile strength of unconfined lap-spliced steel bars using deep residual neural networks and variance-based sensitivity analysis","volume":"10","author":"AlShami","year":"2025","journal-title":"Innovative Infrastructure Solutions"},{"key":"10.1016\/j.eswa.2026.132098_b0025","doi-asserted-by":"crossref","unstructured":"Bang, J., Kim, H., Yoo, Y., Ha, J. W., & Choi, J. (2021). Rainbow memory: Continual learning with a memory of diverse samples. InProceedings of the IEEE\/CVF conference on computer vision and pattern recognition(pp. 8218-8227). https:\/\/doi.org\/10.48550\/arXiv.2103.17230.","DOI":"10.1109\/CVPR46437.2021.00812"},{"key":"10.1016\/j.eswa.2026.132098_b0030","doi-asserted-by":"crossref","unstructured":"Bifet, A., & Gavalda, R. (2007, April). Learning from time-changing data with adaptive windowing. InProceedings of the 2007 SIAM international conference on data mining(pp. 443-448). Society for Industrial and Applied Mathematics. https:\/\/doi.org\/10.1137\/1.9781611972771.42.","DOI":"10.1137\/1.9781611972771.42"},{"key":"10.1016\/j.eswa.2026.132098_b0035","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation.arXiv preprint arXiv:1406.1078. https:\/\/doi.org\/10.48550\/arXiv.1406.1078.","DOI":"10.3115\/v1\/D14-1179"},{"key":"10.1016\/j.eswa.2026.132098_b0040","article-title":"Bayesian estimation of hydrological model parameters in the signature-domain: Aiming for a regional approach","volume":"639","author":"De Sousa Matos","year":"2024","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.eswa.2026.132098_b0045","doi-asserted-by":"crossref","unstructured":"Dohare, S., Hernandez-Garcia, J. F., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2023). Maintaining plasticity in deep continual learning.arXiv preprint arXiv:2306.13812. https:\/\/doi.org\/10.48550\/arXiv.2306.13812.","DOI":"10.21203\/rs.3.rs-3256479\/v1"},{"issue":"8026","key":"10.1016\/j.eswa.2026.132098_b0050","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1038\/s41586-024-07711-7","article-title":"Loss of plasticity in deep continual learning","volume":"632","author":"Dohare","year":"2024","journal-title":"Nature"},{"issue":"3\u20134","key":"10.1016\/j.eswa.2026.132098_b0055","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/0022-1694(94)90057-4","article-title":"Optimal use of the SCE-UA global optimization method for calibrating watershed models","volume":"158","author":"Duan","year":"1994","journal-title":"Journal of hydrology"},{"issue":"10","key":"10.1016\/j.eswa.2026.132098_b0060","doi-asserted-by":"crossref","DOI":"10.1029\/2021WR029655","article-title":"Hydrologic model parameter estimation in ungauged basins using simulated SWOT discharge observations","volume":"57","author":"Elmer","year":"2021","journal-title":"Water Resources Research"},{"issue":"7","key":"10.1016\/j.eswa.2026.132098_b0065","doi-asserted-by":"crossref","DOI":"10.1029\/2005WR004528","article-title":"Multiobjective particle swarm optimization for parameter estimation in hydrology","volume":"42","author":"Gill","year":"2006","journal-title":"Water Resources Research"},{"key":"10.1016\/j.eswa.2026.132098_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.132175","article-title":"Advanced streamflow forecasting for central European rivers: The cutting-edge Kolmogorov-Arnold networks compared to Transformers","volume":"645","author":"Granata","year":"2024","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.eswa.2026.132098_b0075","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long short-term memory.Supervised sequence labelling with recurrent neural networks, 37\u201345. https:\/\/doi.org\/10.1007\/978-3-642-24797-2_4.","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"10.1016\/j.eswa.2026.132098_b0080","doi-asserted-by":"crossref","unstructured":"Hayes, T. L., Kafle, K., Shrestha, R., Acharya, M., & Kanan, C. (2020, August). Remind your neural network to prevent catastrophic forgetting. InEuropean conference on computer vision(pp. 466\u2013483). Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-58598-3_28.","DOI":"10.1007\/978-3-030-58598-3_28"},{"issue":"3","key":"10.1016\/j.eswa.2026.132098_b0085","doi-asserted-by":"crossref","first-page":"3891","DOI":"10.1007\/s40747-024-01350-1","article-title":"CL-BPUWM: Continuous learning with Bayesian parameter updating and weight memory","volume":"10","author":"He","year":"2024","journal-title":"Complex & Intelligent Systems"},{"issue":"5","key":"10.1016\/j.eswa.2026.132098_b0090","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1029\/WR024i005p00691","article-title":"Comparison of Newton\u2010type and direct search algorithms for calibration of conceptual rainfall\u2010runoff models","volume":"24","author":"Hendrickson","year":"1988","journal-title":"Water Resources Research"},{"issue":"3","key":"10.1016\/j.eswa.2026.132098_b0095","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1061\/(ASCE)HE.1943-5584.0000624","article-title":"Three-dimensional groundwater contamination source identification using adaptive simulated annealing","volume":"18","author":"Jha","year":"2013","journal-title":"Journal of Hydrologic Engineering"},{"issue":"14","key":"10.1016\/j.eswa.2026.132098_b0100","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.5194\/hess-27-2621-2023","article-title":"Knowledge-informed deep learning for hydrological model calibration: An application to Coal Creek Watershed in Colorado","volume":"27","author":"Jiang","year":"2023","journal-title":"Hydrology and Earth System Sciences"},{"key":"10.1016\/j.eswa.2026.132098_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130597","article-title":"Machine learning assessment of hydrological model performance under localized water storage changes through downscaling","volume":"628","author":"Kalu","year":"2024","journal-title":"Journal of Hydrology"},{"issue":"13","key":"10.1016\/j.eswa.2026.132098_b0110","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proceedings of the national academy of sciences"},{"key":"10.1016\/j.eswa.2026.132098_b0115","article-title":"Kolmogorov-arnold networks: Key developments and uses","author":"Kilani","year":"2024","journal-title":"Qeios"},{"issue":"3","key":"10.1016\/j.eswa.2026.132098_b0120","doi-asserted-by":"crossref","first-page":"281","DOI":"10.3233\/IDA-2004-8305","article-title":"Learning drifting concepts: Example selection vs. example weighting","volume":"8","author":"Klinkenberg","year":"2004","journal-title":"Intelligent Data Analysis"},{"issue":"12","key":"10.1016\/j.eswa.2026.132098_b0125","doi-asserted-by":"crossref","first-page":"5859","DOI":"10.5194\/hess-24-5859-2020","article-title":"A framework for seasonal variations of hydrological model parameters: Impact on model results and response to dynamic catchment characteristics","volume":"24","author":"Lan","year":"2020","journal-title":"Hydrology and Earth System Sciences"},{"issue":"11","key":"10.1016\/j.eswa.2026.132098_b0130","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"2002","journal-title":"Proceedings of the IEEE"},{"key":"10.1016\/j.eswa.2026.132098_b0135","unstructured":"Lee, S., Jeon, H., Son, J., & Kim, G. (2024). Learning to continually learn with the Bayesian principle.arXiv preprint arXiv:2405.18758. https:\/\/doi.org\/10.48550\/arXiv.2405.18758."},{"key":"10.1016\/j.eswa.2026.132098_b0140","doi-asserted-by":"crossref","unstructured":"Li, Y., Wu, W., Luo, X., Zheng, M., Zhang, Y., & Peng, B. (2023, November). A survey: Navigating the landscape of incremental learning techniques and trends. In2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)(pp. 163\u2013169). IEEE. https:\/\/doi.org\/10.1109\/ISKE60036.2023.10481497.","DOI":"10.1109\/ISKE60036.2023.10481497"},{"issue":"12","key":"10.1016\/j.eswa.2026.132098_b0145","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"10.1016\/j.eswa.2026.132098_b0150","first-page":"21","article-title":"Application of surrogate modeling parameter calibration method in TOPKAPI model","volume":"52","author":"Ling","year":"2024","journal-title":"Journal of Hohai University (Natural Sciences)"},{"key":"10.1016\/j.eswa.2026.132098_b0155","unstructured":"Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Solja\u010di\u0107, M., & Tegmark, M. (2024). Kan: Kolmogorov-arnold networks.arXiv preprint arXiv:2404.19756. https:\/\/doi.org\/10.48550\/arXiv.2404.19756."},{"key":"10.1016\/j.eswa.2026.132098_b0160","doi-asserted-by":"crossref","unstructured":"Liu, H., Yang, Y., & Wang, X. (2021, May). Overcoming catastrophic forgetting in graph neural networks. InProceedings of the AAAI conference on artificial intelligence(Vol. 35, No. 10, pp. 8653\u20138661). https:\/\/doi.org\/10.1609\/aaai.v35i10.17049.","DOI":"10.1609\/aaai.v35i10.17049"},{"key":"10.1016\/j.eswa.2026.132098_b0165","doi-asserted-by":"crossref","unstructured":"Loh, W. Y. (2011). Classification and regression trees.Wiley interdisciplinary reviews: data mining and knowledge discovery,1(1), 14\u201323. https:\/\/doi.org\/10.1002\/widm.8.","DOI":"10.1002\/widm.8"},{"key":"10.1016\/j.eswa.2026.132098_b0170","unstructured":"Lopez-Paz, D., & Ranzato, M. A. (2017). Gradient episodic memory for continual learning.Advances in neural information processing systems,30. https:\/\/doi.org\/10.48550\/arXiv.1706.08840."},{"issue":"1","key":"10.1016\/j.eswa.2026.132098_b0175","doi-asserted-by":"crossref","first-page":"349","DOI":"10.3390\/smartcities4010021","article-title":"Concept drift adaptation techniques in distributed environment for real-world data streams","volume":"4","author":"Mehmood","year":"2021","journal-title":"Smart Cities"},{"issue":"2","key":"10.1016\/j.eswa.2026.132098_b0180","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1061\/(ASCE)0733-9429(1997)123:2(137)","article-title":"Parameter estimation of nonlinear Muskingum models using genetic algorithm","volume":"123","author":"Mohan","year":"1997","journal-title":"Journal of hydraulic engineering"},{"key":"10.1016\/j.eswa.2026.132098_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.earscirev.2024.104956","article-title":"An overview of approaches for reducing uncertainties in hydrological forecasting: Progress and challenges","volume":"258","author":"Panchanathan","year":"2024","journal-title":"Earth-Science Reviews"},{"key":"10.1016\/j.eswa.2026.132098_b0190","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual lifelong learning with neural networks: A review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural networks"},{"issue":"2","key":"10.1016\/j.eswa.2026.132098_b0195","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1093\/comjnl\/7.2.155","article-title":"An efficient method for finding the minimum of a function of several variables without calculating derivatives","volume":"7","author":"Powell","year":"1964","journal-title":"The Computer Journal"},{"issue":"1","key":"10.1016\/j.eswa.2026.132098_b0200","first-page":"1","article-title":"Physical interpretaion parameters of Xin\u2019anjiang-Haihe model and its application","volume":"53","author":"Qiaoling","year":"2025","journal-title":"Journal of Hohai University (Natural Sciences)"},{"issue":"1","key":"10.1016\/j.eswa.2026.132098_b0205","doi-asserted-by":"crossref","DOI":"10.1080\/10298436.2023.2268808","article-title":"Analysing Witczak 1-37A, Witczak 1-40D and Modified Hirsch Models for asphalt dynamic modulus prediction using global sensitivity analysis","volume":"24","author":"Owais","year":"2023","journal-title":"International Journal of Pavement Engineering"},{"key":"10.1016\/j.eswa.2026.132098_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2024.138693","article-title":"Preprocessing and postprocessing analysis for hot-mix asphalt dynamic modulus experimental data","volume":"450","author":"Owais","year":"2024","journal-title":"Construction and Building Materials"},{"key":"10.1016\/j.eswa.2026.132098_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2023.134775","article-title":"Global sensitivity analysis for studying hot-mix asphalt dynamic modulus parameters","volume":"413","author":"Owais","year":"2024","journal-title":"Construction and Building Materials"},{"key":"10.1016\/j.eswa.2026.132098_b0220","doi-asserted-by":"crossref","unstructured":"Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017). icarl: Incremental classifier and representation learning. InProceedings of the IEEE conference on Computer Vision and Pattern Recognition(pp. 2001\u20132010). https:\/\/doi.org\/10.1109\/CVPR.2017.587.","DOI":"10.1109\/CVPR.2017.587"},{"issue":"6","key":"10.1016\/j.eswa.2026.132098_b0225","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: A probabilistic model for information storage and organization in the brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychological review"},{"issue":"3","key":"10.1016\/j.eswa.2026.132098_b0230","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1093\/comjnl\/3.3.175","article-title":"An automatic method for finding the greatest or least value of a function","volume":"3","author":"Rosenbrock","year":"1960","journal-title":"The Computer Journal"},{"key":"10.1016\/j.eswa.2026.132098_b0235","unstructured":"Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., & Hadsell, R. (2016). Progressive neural networks.arXiv preprint arXiv:1606.04671. https:\/\/doi.org\/10.48550\/arXiv.1606.04671."},{"key":"10.1016\/j.eswa.2026.132098_b0240","unstructured":"Serra, J., Suris, D., Miron, M., & Karatzoglou, A. (2018, July). Overcoming catastrophic forgetting with hard attention to the task. InInternational conference on machine learning(pp. 4548-4557). PMLR. https:\/\/doi.org\/10.48550\/arXiv.1801.01423."},{"key":"10.1016\/j.eswa.2026.132098_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2024.118533","article-title":"Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques","volume":"246","author":"Shi","year":"2024","journal-title":"Environmental Research"},{"issue":"2","key":"10.1016\/j.eswa.2026.132098_b0250","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3743128","article-title":"A survey on kolmogorov-arnold network","volume":"58","author":"Somvanshi","year":"2025","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.eswa.2026.132098_b0255","doi-asserted-by":"crossref","unstructured":"Ta, H. T. (2024, December). BSRBF-KAN: a combination of B-splines and radial basis functions in Kolmogorov-Arnold networks. InInternational Symposium on Information and Communication Technology(pp. 3\u201315). Singapore: Springer Nature Singapore. https:\/\/doi.org\/10.1007\/978-981-96-4288-5_1.","DOI":"10.1007\/978-981-96-4288-5_1"},{"key":"10.1016\/j.eswa.2026.132098_b0260","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.neunet.2023.10.039","article-title":"A survey on few-shot class-incremental learning","volume":"169","author":"Tian","year":"2024","journal-title":"Neural Networks"},{"issue":"11","key":"10.1016\/j.eswa.2026.132098_b0270","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1007\/s42452-019-1433-0","article-title":"Data stream mining: Methods and challenges for handling concept drift","volume":"1","author":"Wares","year":"2019","journal-title":"SN Applied Sciences"},{"issue":"4","key":"10.1016\/j.eswa.2026.132098_b0275","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1007\/s00477-022-02336-6","article-title":"Real-time error correction for flood forecasting based on machine learning ensemble method and its uncertainty assessment","volume":"37","author":"Xu","year":"2023","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"key":"10.1016\/j.eswa.2026.132098_b0280","doi-asserted-by":"crossref","unstructured":"Yan, L., Feng, J., Wu, Y., & Hang, T. (2019, December). Data-driven fast real-time flood forecasting model for processing concept drift. InInternational Conference on Cloud Computing(pp. 363\u2013374). Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-48513-9_30.","DOI":"10.1007\/978-3-030-48513-9_30"},{"issue":"5","key":"10.1016\/j.eswa.2026.132098_b0290","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00704-025-05470-7","article-title":"Flood level prediction model based on Kolmogorov-Arnold Networks: An improved deep learning approach","volume":"156","author":"Zhao","year":"2025","journal-title":"Theoretical and Applied Climatology"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426010110?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426010110?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:44:17Z","timestamp":1776156257000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426010110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":56,"alternative-id":["S0957417426010110"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132098","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Incremental learning\u2013based Kolmogorov\u2013Arnold Networks for adaptive hydrological parameter optimization of flood forecasting","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132098","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132098"}}