{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:14:34Z","timestamp":1763100874357,"version":"3.45.0"},"reference-count":28,"publisher":"Tech Science Press","issue":"2","license":[{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2024.058277","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T04:13:16Z","timestamp":1734581596000},"page":"2951-2967","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Study on Sediment Removal Method of Reservoir Based on Double Branch Convolution"],"prefix":"10.32604","volume":"82","author":[{"given":"Hailong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchao","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinjie","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1089\/big.2020.0159","article-title":"Deep learning for time series forecasting: A survey","volume":"9","author":"Torres","year":"2021","journal-title":"IEEE Big Data"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.3390\/w9060440","article-title":"Methodology for analyzing and predicting the runoff and sediment into a reservoir","volume":"9","author":"Hao","year":"2017, Art. no. 440","journal-title":"Water"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"19005","DOI":"10.1038\/s41598-021-98394-x","article-title":"Prediction research on sedimentation balance of three gorges reservoir under new conditions of water and sediment","volume":"11","author":"Chen","year":"2021","journal-title":"Sci. Rep."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1007\/s00477-021-01982-6","article-title":"Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir","volume":"35","author":"Idrees","year":"2021","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1061\/(ASCE)0733-9429(2002)128:6(588)","article-title":"Prediction of sediment load concentration in rivers using artificial neural network model","volume":"128","author":"Nagy","year":"2002","journal-title":"J. Hydraul. Eng."},{"key":"ref6","doi-asserted-by":"crossref","first-page":"7826","DOI":"10.1038\/s41598-021-87415-4","article-title":"Suspended sediment load prediction using long short-term memory neural network","volume":"11","author":"AlDahoul","year":"2021","journal-title":"Sci. Rep."},{"key":"ref7","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1007\/s12517-020-05772-2","article-title":"Predicting reservoir volume reduction using artificial neural network","volume":"13","author":"Iraji","year":"2020","journal-title":"Arab J. Geosci"},{"key":"ref8","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Proc. 35th Int. Conf. Neural Inform. Process. Syst. (NeurIPS 2021)"},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.3390\/app14010012","article-title":"Predicting river discharge in the niger river basin: A deep learning approach","volume":"14","author":"Ogunjo","year":"2023, Art. no. 12","journal-title":"Appl. Sci."},{"key":"ref10","first-page":"11121","article-title":"Are transformers effective for time series forecasting?","volume":"37","author":"Zeng","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1007\/s11269-014-0870-1","article-title":"ANN based sediment prediction model utilizing different input scenarios","volume":"29","author":"Afan","year":"2015","journal-title":"Water Resour. Manage."},{"key":"ref12","article-title":"A two-branch neural network for gas-bearing prediction using latent space adaptation for data augmentation\u2014An application for deep carbonate reservoirs","author":"Ma","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref13","doi-asserted-by":"crossref","first-page":"14368","DOI":"10.1109\/JSTARS.2024.3440640","article-title":"Dual-branch feature interaction network for coastal wetland classification using sentinel-1 and 2","volume":"17","author":"Xu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"unstructured":"S. Bai, J. Z. Kolter, and V. J. Koltun, \u201cAn empirical evaluation of generic convolutional and recurrent networks for sequence modeling,\u201d 2018. arXiv:1803.01271.","key":"ref14"},{"key":"ref15","series-title":"36th Conf. Neural Inform. Process. Syst. (NeurIPS 2022)","first-page":"5816","article-title":"Scinet: Time series modeling and forecasting with sample convolution and interaction","volume":"35","author":"Liu","year":"2022"},{"key":"ref16","series-title":"Eleventh Int. Conf. Learn. Represent.","article-title":"MICN: Multi-scale local and global context modeling for long-term series forecasting","author":"Wang","year":"2023"},{"unstructured":"H. Wu, T. Hu, Y. Liu, H. Zhou, J. Wang, and M. J. Long, \u201cTimesNet: Temporal 2D-variation modeling for general time series analysis,\u201d 2022, arXiv:2210.02186.","key":"ref17"},{"key":"ref18","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"unstructured":"S. J. Ioffe, \u201cBatch normalization: Accelerating deep network training by reducing internal covariate shift,\u201d 2015, arXiv:1502.03167.","key":"ref19"},{"unstructured":"A. G. J. Howard, \u201cMobileNets: Efficient convolutional neural networks for mobile vision applications,\u201d 2017, arXiv:1704.04861.","key":"ref20"},{"key":"ref21","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"1251","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"key":"ref22","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s11263-018-1125-z","article-title":"From BoW to CNN: Two decades of texture representation for texture classification","volume":"127","author":"Liu","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref24","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118004","article-title":"Deep multi-scale Gaussian residual networks for contextual-aware translation initiation site recognition","volume":"207","author":"Guo","year":"2022, Art. no. 118004","journal-title":"Expert Syst. Appl."},{"unstructured":"I. J. Loshchilov, \u201cDecoupled weight decay regularization,\u201d 2017, arXiv:1711.05101.","key":"ref25"},{"unstructured":"Y. Liu et al., \u201ciTransformer: Inverted transformers are effective for time series forecasting,\u201d 2023, arXiv:2310.06625.","key":"ref26"},{"unstructured":"Z. Gong, Y. Tang, and J. J. Liang, \u201cPatchMixer: A patch-mixing architecture for long-term time series forecasting,\u201d 2023, arXiv:2310.00655.","key":"ref27"},{"unstructured":"S. A. Chen, C. L. Li, N. Yoder, S. O. Arik, and T. J. Pfister, \u201cTSMixer: An All-MLP architecture for time series forecasting,\u201d 2023, arXiv:2303.06053.","key":"ref28"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-2\/TSP_CMC_58277\/TSP_CMC_58277.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:10:53Z","timestamp":1763100653000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n2\/59469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.058277","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-09-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-17","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}