{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T21:22:35Z","timestamp":1782249755900,"version":"3.54.5"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The special project for collaborative innovation of science and technology in 2021","award":["202121206"],"award-info":[{"award-number":["202121206"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s12145-024-01524-y","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T04:28:17Z","timestamp":1733977697000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A singular spectrum analysis-enhanced BiTCN-selfattention model for runoff prediction"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1367-5886","authenticated-orcid":false,"given":"Wen-chuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng-rui","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-yang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"1524_CR1","doi-asserted-by":"publisher","first-page":"140715","DOI":"10.1016\/j.jclepro.2024.140715","volume":"441","author":"AA Ahmed","year":"2024","unstructured":"Ahmed AA, Sayed S, Abdoulhalik A, Moutari S, Oyedele L (2024) Applications of machine learning to water resources management: A review of present status and future opportunities. J Clean Prod 441:140715. https:\/\/doi.org\/10.1016\/j.jclepro.2024.140715","journal-title":"J Clean Prod"},{"key":"1524_CR2","doi-asserted-by":"publisher","first-page":"3576","DOI":"10.3390\/w15203576","volume":"15","author":"MV Anaraki","year":"2023","unstructured":"Anaraki MV, Achite M, Farzin S, Elshaboury N, Al-Ansari N, Elkhrachy I (2023) Modeling of Monthly Rainfall-Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria. Water 15:3576. https:\/\/doi.org\/10.3390\/w15203576","journal-title":"Water"},{"key":"1524_CR3","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1080\/02626667.2021.1948550","volume":"66","author":"L Bertassello","year":"2021","unstructured":"Bertassello L, Levy MC, M\u00fcller MF (2021) Sociohydrology, ecohydrology, and the space-time dynamics of human-altered catchments. Hydrol Sci J 66:1393\u20131408. https:\/\/doi.org\/10.1080\/02626667.2021.1948550","journal-title":"Hydrol Sci J"},{"key":"1524_CR4","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.3390\/w14091502","volume":"14","author":"Z Cao","year":"2022","unstructured":"Cao Z, Wang S, Luo P, Xie D, Zhu W (2022) Watershed Ecohydrological Processes in a Changing Environment: Opportunities and Challenges. Water 14:1502. https:\/\/doi.org\/10.3390\/w14091502","journal-title":"Water"},{"key":"1524_CR5","doi-asserted-by":"publisher","first-page":"126945","DOI":"10.1016\/j.jhydrol.2021.126945","volume":"603","author":"S Chen","year":"2021","unstructured":"Chen S, Ren M, Sun W (2021) Combining two-stage decomposition based machine learning methods for annual runoff forecasting. J Hydrol 603:126945. https:\/\/doi.org\/10.1016\/j.jhydrol.2021.126945","journal-title":"J Hydrol"},{"key":"1524_CR6","doi-asserted-by":"publisher","first-page":"131332","DOI":"10.1016\/j.energy.2024.131332","volume":"298","author":"J Dai","year":"2024","unstructured":"Dai J, Fu L-h (2024) A wind speed forecasting model using nonlinear auto-regressive model optimized by the hybrid chaos-cloud salp swarm algorithm. Energy 298:131332. https:\/\/doi.org\/10.1016\/j.energy.2024.131332","journal-title":"Energy"},{"key":"1524_CR7","doi-asserted-by":"publisher","first-page":"025116","DOI":"10.1088\/2631-8695\/ad4cb6","volume":"6","author":"X Dang","year":"2024","unstructured":"Dang X, Yin X, Liu J, Wu J, Wang X, Liu Y, Sun S (2024) Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model. Eng Res Express 6:025116. https:\/\/doi.org\/10.1088\/2631-8695\/ad4cb6","journal-title":"Eng Res Express"},{"key":"1524_CR8","doi-asserted-by":"publisher","first-page":"102119","DOI":"10.1016\/j.ecoinf.2023.102119","volume":"75","author":"A Dehghani","year":"2023","unstructured":"Dehghani A, Moazam HMZH, Mortazavizadeh F, Ranjbar V, Mirzaei M, Mortezavi S, Ng JL, Dehghani A (2023) Comparative evaluation of LSTM, CNN, and ConvLSTM for hourly short-term streamflow forecasting using deep learning approaches. Eco Inform 75:102119. https:\/\/doi.org\/10.1016\/j.ecoinf.2023.102119","journal-title":"Eco Inform"},{"key":"1524_CR9","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1007\/s11269-024-03748-5","volume":"38","author":"J Dong","year":"2024","unstructured":"Dong J, Wang Z, Wu J, Cui X, Pei R (2024) A Novel Runoff Prediction Model Based on Support Vector Machine and Gate Recurrent unit with Secondary Mode Decomposition. Water Resour Manag 38:1655\u20131674. https:\/\/doi.org\/10.1007\/s11269-024-03748-5","journal-title":"Water Resour Manag"},{"key":"1524_CR10","doi-asserted-by":"publisher","unstructured":"Ge Y, Zhang F, Ren Y (2022) Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism. Front Energy Res 10. https:\/\/doi.org\/10.3389\/fenrg.2022.810490","DOI":"10.3389\/fenrg.2022.810490"},{"key":"1524_CR11","doi-asserted-by":"publisher","first-page":"7833","DOI":"10.1109\/JSEN.2019.2923982","volume":"21","author":"Z Han","year":"2021","unstructured":"Han Z, Zhao J, Leung H, Ma KF, Wang W (2021) A Review of Deep Learning Models for Time Series Prediction. IEEE Sens J 21:7833\u20137848. https:\/\/doi.org\/10.1109\/JSEN.2019.2923982","journal-title":"IEEE Sens J"},{"key":"1524_CR12","doi-asserted-by":"publisher","first-page":"7279","DOI":"10.1007\/s00521-024-09460-0","volume":"36","author":"J Huang","year":"2024","unstructured":"Huang J, Wu R, Li J (2024) Complex network robustness prediction using attention-augmented CNN. Neural Comput Appl 36:7279\u20137294. https:\/\/doi.org\/10.1007\/s00521-024-09460-0","journal-title":"Neural Comput Appl"},{"key":"1524_CR13","doi-asserted-by":"publisher","first-page":"105896","DOI":"10.1016\/j.envsoft.2023.105896","volume":"172","author":"HK Ji","year":"2024","unstructured":"Ji HK, Mirzaei M, Lai SH, Dehghani A, Dehghani A (2024) Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation. Environ Model Softw 172:105896. https:\/\/doi.org\/10.1016\/j.envsoft.2023.105896","journal-title":"Environ Model Softw"},{"key":"1524_CR14","doi-asserted-by":"publisher","first-page":"114517","DOI":"10.1016\/j.measurement.2024.114517","volume":"230","author":"J Jin","year":"2024","unstructured":"Jin J, Jin Q, Chen J, Wang C, Li M, Yu L (2024) Prediction of the tunnelling advance speed of a super-large-diameter shield machine based on a KF-CNN-BiGRU hybrid neural network. Measurement 230:114517. https:\/\/doi.org\/10.1016\/j.measurement.2024.114517","journal-title":"Measurement"},{"key":"1524_CR15","doi-asserted-by":"publisher","unstructured":"Karthick Myilvahanan J, Mohana Sundaram N (2024) Support vector machine-based stock market prediction using long short-term memory and convolutional neural network with aquila circle inspired optimization. Network:1\u201336. https:\/\/doi.org\/10.1080\/0954898x.2024.2358957","DOI":"10.1080\/0954898x.2024.2358957"},{"key":"1524_CR16","doi-asserted-by":"publisher","first-page":"100551","DOI":"10.1016\/j.mlwa.2024.100551","volume":"16","author":"K Khand","year":"2024","unstructured":"Khand K, Senay GB (2024) Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States. Mach Learn Appl 16:100551. https:\/\/doi.org\/10.1016\/j.mlwa.2024.100551","journal-title":"Mach Learn Appl"},{"key":"1524_CR17","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.3390\/rs15123069","volume":"15","author":"A Kharakhashyan","year":"2023","unstructured":"Kharakhashyan A, Maltseva O (2023) Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region. Remote Sensing 15:3069. https:\/\/doi.org\/10.3390\/rs15123069","journal-title":"Remote Sensing"},{"key":"1524_CR18","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.physa.2017.04.108","volume":"483","author":"L Lai","year":"2017","unstructured":"Lai L, Guo K (2017) The performance of one belt and one road exchange rate: Based on improved singular spectrum analysis. Physica A 483:299\u2013308. https:\/\/doi.org\/10.1016\/j.physa.2017.04.108","journal-title":"Physica A"},{"key":"1524_CR19","doi-asserted-by":"publisher","DOI":"10.3389\/fenvs.2022.1049840","author":"Y Lei","year":"2022","unstructured":"Lei Y, Qingwen L, Cong J, Pengtao Y, Zheng R, Bin L, Zhangjun L (2022) Climate-informed monthly runoff prediction model using machine learning and feature importance analysis. Front Environ Sci. https:\/\/doi.org\/10.3389\/fenvs.2022.1049840","journal-title":"Front Environ Sci"},{"key":"1524_CR20","doi-asserted-by":"publisher","first-page":"575","DOI":"10.3390\/w13040575","volume":"13","author":"Z Li","year":"2021","unstructured":"Li Z, Kang L, Zhou L, Zhu M (2021) Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction. Water 13:575. https:\/\/doi.org\/10.3390\/w13040575","journal-title":"Water"},{"key":"1524_CR21","doi-asserted-by":"publisher","first-page":"5448","DOI":"10.1080\/01431161.2023.2249597","volume":"44","author":"S Li","year":"2023","unstructured":"Li S, Mao J, Li Z (2023) An EEMD-SVD method based on gray wolf optimization algorithm for lidar signal noise reduction. Int J Remote Sens 44:5448\u20135472. https:\/\/doi.org\/10.1080\/01431161.2023.2249597","journal-title":"Int J Remote Sens"},{"key":"1524_CR22","doi-asserted-by":"publisher","first-page":"108536","DOI":"10.1016\/j.measurement.2020.108536","volume":"173","author":"A Logan","year":"2021","unstructured":"Logan A, Cava DG, Li\u015bkiewicz G (2021) Singular spectrum analysis as a tool for early detection of centrifugal compressor flow instability. Measurement 173:108536. https:\/\/doi.org\/10.1016\/j.measurement.2020.108536","journal-title":"Measurement"},{"key":"1524_CR23","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1080\/15435075.2023.2194373","volume":"20","author":"J Luo","year":"2023","unstructured":"Luo J, Chen T, Xiao F, Peng Y (2023) Remaining useful life prediction of PEMFC based on CNN-Birnn model. Int J Green Energy 20:1729\u20131740. https:\/\/doi.org\/10.1080\/15435075.2023.2194373","journal-title":"Int J Green Energy"},{"key":"1524_CR24","doi-asserted-by":"publisher","first-page":"13384","DOI":"10.3390\/su132313384","volume":"13","author":"M Mirzaei","year":"2021","unstructured":"Mirzaei M, Yu H, Dehghani A, Galavi H, Shokri V, MohsenzadehKarimi S, Sookhak M (2021) A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation. Sustainability 13:13384. https:\/\/doi.org\/10.3390\/su132313384","journal-title":"Sustainability"},{"key":"1524_CR25","doi-asserted-by":"publisher","first-page":"5693","DOI":"10.3390\/app13095693","volume":"13","author":"C Mo","year":"2023","unstructured":"Mo C, Yan Z, Ma R, Lei X, Deng Y, Lai S, Huang K, Mo X (2023) Investigation of the EWT\u2013PSO\u2013SVM Model for Runoff Forecasting in the Karst Area. Appl Sci 13:5693. https:\/\/doi.org\/10.3390\/app13095693","journal-title":"Appl Sci"},{"key":"1524_CR26","doi-asserted-by":"publisher","first-page":"5717","DOI":"10.1007\/s40808-024-02088-y","volume":"10","author":"AU Muhammad","year":"2024","unstructured":"Muhammad AU, Muazu T, Ying H, Ba AF, Tijjani S, Adam JM, Bello AU, Bala MM, Ali MH, Dabai US, Kumshe UMM, Yahaya MS (2024) Enhanced streamflow forecasting using attention-based neural network models: a comparative study in MOPEX basins. Model Earth Syst Environ 10:5717\u20135734. https:\/\/doi.org\/10.1007\/s40808-024-02088-y","journal-title":"Model Earth Syst Environ"},{"key":"1524_CR27","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1080\/15435075.2023.2244589","volume":"21","author":"S Mulewa","year":"2024","unstructured":"Mulewa S, Parmar AM, De A (2024) Attention based Transformer coupled with convoluted neural network for ultra-short- and short-term forecasting of multiple wind farms. Int J Green Energy 21:1238\u20131252. https:\/\/doi.org\/10.1080\/15435075.2023.2244589","journal-title":"Int J Green Energy"},{"key":"1524_CR28","doi-asserted-by":"publisher","first-page":"118343","DOI":"10.1016\/j.enconman.2024.118343","volume":"307","author":"J Pang","year":"2024","unstructured":"Pang J, Dong S (2024) A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism. Energy Convers Manage 307:118343. https:\/\/doi.org\/10.1016\/j.enconman.2024.118343","journal-title":"Energy Convers Manage"},{"key":"1524_CR29","doi-asserted-by":"publisher","unstructured":"Sheng Z, Cao Y, Yang Y, Feng Z, Shi K, Huang T, Wen S (2024) Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/tnnls.2024.3411166","DOI":"10.1109\/tnnls.2024.3411166"},{"key":"1524_CR30","doi-asserted-by":"publisher","first-page":"4107","DOI":"10.3390\/en14144107","volume":"14","author":"A Stratigakos","year":"2021","unstructured":"Stratigakos A, Bachoumis A, Vita V, Zafiropoulos E (2021) Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks. Energies 14:4107. https:\/\/doi.org\/10.3390\/en14144107","journal-title":"Energies"},{"key":"1524_CR31","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.3390\/BIOM13111643","volume":"13","author":"S Teragawa","year":"2023","unstructured":"Teragawa S, Wang L (2023) ConF: A Deep Learning Model Based on BiLSTM, CNN, and Cross Multi-Head Attention Mechanism for Noncoding RNA Family Prediction. Biomolecules 13:1643. https:\/\/doi.org\/10.3390\/BIOM13111643","journal-title":"Biomolecules"},{"key":"1524_CR32","doi-asserted-by":"publisher","first-page":"116225","DOI":"10.1016\/j.energy.2019.116225","volume":"189","author":"K Wang","year":"2019","unstructured":"Wang K, Qi X, Liu H (2019) Photovoltaic power forecasting based LSTM-Convolutional Network. Energy 189:116225. https:\/\/doi.org\/10.1016\/j.energy.2019.116225","journal-title":"Energy"},{"key":"1524_CR33","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1007\/s11269-021-02920-5","volume":"35","author":"W-c Wang","year":"2021","unstructured":"Wang W-c, Du Y-j, Chau K-w, Xu D-m, Liu C-j, Ma Q (2021) An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network. Water Resour Manage 35:4695\u20134726. https:\/\/doi.org\/10.1007\/s11269-021-02920-5","journal-title":"Water Resour Manage"},{"key":"1524_CR34","doi-asserted-by":"publisher","first-page":"1770","DOI":"10.1007\/s10489-024-05271-x","volume":"54","author":"C-H Wang","year":"2024","unstructured":"Wang C-H, Yuan J, Zeng Y, Lin S (2024a) A deep learning integrated framework for predicting stock index price and fluctuation via singular spectrum analysis and particle swarm optimization. Appl Intell 54:1770\u20131797. https:\/\/doi.org\/10.1007\/s10489-024-05271-x","journal-title":"Appl Intell"},{"key":"1524_CR35","doi-asserted-by":"publisher","first-page":"23550","DOI":"10.1038\/s41598-024-74329-0","volume":"14","author":"WC Wang","year":"2024","unstructured":"Wang WC, Gu M, Hong YH, Hu XX, Zang HF, Chen XN, Jin YG (2024b) SMGformer: integrating STL and multi-head self-attention in deep learning model for multi-step runoff forecasting. Sci Rep 14:23550. https:\/\/doi.org\/10.1038\/s41598-024-74329-0","journal-title":"Sci Rep"},{"key":"1524_CR36","doi-asserted-by":"publisher","first-page":"131996","DOI":"10.1016\/j.jhydrol.2024.131996","volume":"643","author":"WC Wang","year":"2024","unstructured":"Wang WC, Tian WC, Hu XX, Hong YH, Chai FX, Xu DM (2024c) DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion. J Hydrol 643:131996. https:\/\/doi.org\/10.1016\/j.jhydrol.2024.131996","journal-title":"J Hydrol"},{"key":"1524_CR37","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s12145-023-01212-3","volume":"17","author":"Y-y Wang","year":"2024","unstructured":"Wang Y-y, Wang W-c, Xu D-m, Zhao Y-w, Zang H-f (2024d) A compound approach for ten-day runoff prediction by coupling wavelet denoising, attention mechanism, and LSTM based on GPU parallel acceleration technology. Earth Sci Inf 17:1281\u20131299. https:\/\/doi.org\/10.1007\/s12145-023-01212-3","journal-title":"Earth Sci Inf"},{"key":"1524_CR38","doi-asserted-by":"publisher","first-page":"3135","DOI":"10.1007\/s11269-024-03806-y","volume":"38","author":"WC Wang","year":"2024","unstructured":"Wang WC, Du YJ, Chau KW, Cheng CT, Xu DM, Zhuang WT (2024) Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series. Water Resour Manag 38:3135\u20133152. https:\/\/doi.org\/10.1007\/s11269-024-03806-y","journal-title":"Water Resour Manag"},{"key":"1524_CR39","doi-asserted-by":"publisher","first-page":"3717","DOI":"10.3390\/w15213717","volume":"15","author":"H Wei","year":"2023","unstructured":"Wei H, Wang Y, Liu J, Cao Y (2023) Monthly Runoff Prediction by Combined Models Based on Secondary Decomposition at the Wulong Hydrological Station in the Yangtze River Basin. Water 15:3717. https:\/\/doi.org\/10.3390\/w15213717","journal-title":"Water"},{"key":"1524_CR40","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1007\/s11440-023-02050-9","volume":"19","author":"H Wen","year":"2024","unstructured":"Wen H, Xiao J, Xiang X, Wang X, Zhang W (2024) Singular spectrum analysis-based hybrid PSO-GSA-SVR model for predicting displacement of step-like landslides: a case of Jiuxianping landslide. Acta Geotech 19:1835\u20131852. https:\/\/doi.org\/10.1007\/s11440-023-02050-9","journal-title":"Acta Geotech"},{"key":"1524_CR41","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1186\/s40623-023-01937-x","volume":"75","author":"K Wu","year":"2023","unstructured":"Wu K, Liu X, Jin X, Chang X, Sun H, Guo J (2023) Precise prediction of polar motion using sliding multilayer perceptron method combining singular spectrum analysis and autoregressive moving average model. Earth, Planets Space 75:179. https:\/\/doi.org\/10.1186\/s40623-023-01937-x","journal-title":"Earth, Planets Space"},{"key":"1524_CR42","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.3390\/en17081815","volume":"17","author":"X Xiang","year":"2024","unstructured":"Xiang X, Yuan T, Cao G, Zheng Y (2024) Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm. Energies 17:1815. https:\/\/doi.org\/10.3390\/en17081815","journal-title":"Energies"},{"key":"1524_CR43","doi-asserted-by":"publisher","first-page":"3390","DOI":"10.3390\/w13233390","volume":"13","author":"Z Xu","year":"2021","unstructured":"Xu Z, Zhou J, Mo L, Jia B, Yang Y, Fang W, Qin Z (2021) A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework. Water 13:3390. https:\/\/doi.org\/10.3390\/w13233390","journal-title":"Water"},{"key":"1524_CR44","doi-asserted-by":"publisher","first-page":"943","DOI":"10.2166\/hydro.2023.172","volume":"25","author":"D-m Xu","year":"2023","unstructured":"Xu D-m, Wang X, Wang W-c, Chau K-w, Zang H-f (2023) Improved monthly runoff time series prediction using the SOA\u2013SVM model based on ICEEMDAN\u2013WD decomposition. J Hydroinf 25:943\u2013970. https:\/\/doi.org\/10.2166\/hydro.2023.172","journal-title":"J Hydroinf"},{"key":"1524_CR45","doi-asserted-by":"publisher","first-page":"121719","DOI":"10.1016\/j.eswa.2023.121719","volume":"238","author":"D-m Xu","year":"2024","unstructured":"Xu D-m, Hu X-x, Wang W-c, Chau K-w, Zang H-f, Wang J (2024a) A new hybrid model for monthly runoff prediction using ELMAN neural network based on decomposition-integration structure with local error correction method. Expert Syst Appl 238:121719. https:\/\/doi.org\/10.1016\/j.eswa.2023.121719","journal-title":"Expert Syst Appl"},{"key":"1524_CR46","doi-asserted-by":"publisher","first-page":"130558","DOI":"10.1016\/j.jhydrol.2023.130558","volume":"629","author":"D-m Xu","year":"2024","unstructured":"Xu D-m, Li Z, Wang W-c (2024b) An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy. J Hydrol 629:130558. https:\/\/doi.org\/10.1016\/j.jhydrol.2023.130558","journal-title":"J Hydrol"},{"key":"1524_CR47","doi-asserted-by":"publisher","first-page":"130388","DOI":"10.1016\/j.energy.2024.130388","volume":"292","author":"X Yan","year":"2024","unstructured":"Yan X, Ji X, Meng Q, Sun H, Lei Y (2024) A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism. Energy 292:130388. https:\/\/doi.org\/10.1016\/j.energy.2024.130388","journal-title":"Energy"},{"key":"1524_CR48","doi-asserted-by":"publisher","first-page":"82179","DOI":"10.1007\/s11356-023-28191-8","volume":"30","author":"C Yang","year":"2023","unstructured":"Yang C, Jiang Y, Liu Y, Liu S, Liu F (2023) A novel model for runoff prediction based on the ICEEMDAN-NGO-LSTM coupling. Environ Sci Pollut Res 30:82179\u201382188. https:\/\/doi.org\/10.1007\/s11356-023-28191-8","journal-title":"Environ Sci Pollut Res"},{"key":"1524_CR49","doi-asserted-by":"publisher","first-page":"117279","DOI":"10.1016\/j.oceaneng.2024.117279","volume":"299","author":"Y Yang","year":"2024","unstructured":"Yang Y, Han L, Qiu C, Zhao Y (2024a) A short-term wave energy forecasting model using two-layer decomposition and LSTM-attention. Ocean Eng 299:117279. https:\/\/doi.org\/10.1016\/j.oceaneng.2024.117279","journal-title":"Ocean Eng"},{"key":"1524_CR50","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.3390\/w16111552","volume":"16","author":"Y Yang","year":"2024","unstructured":"Yang Y, Li W, Liu D (2024b) Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition. Water 16:1552","journal-title":"Water"},{"key":"1524_CR51","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jher.2017.10.005","volume":"18","author":"X Yu","year":"2018","unstructured":"Yu X, Zhang X, Qin H (2018) A data-driven model based on Fourier transform and support vector regression for monthly reservoir inflow forecasting. J Hydro-Environ Res 18:12\u201324. https:\/\/doi.org\/10.1016\/j.jher.2017.10.005","journal-title":"J Hydro-Environ Res"},{"key":"1524_CR52","doi-asserted-by":"publisher","first-page":"124293","DOI":"10.1016\/j.jhydrol.2019.124293","volume":"582","author":"X Yu","year":"2020","unstructured":"Yu X, Wang Y, Wu L, Chen G, Wang L, Qin H (2020) Comparison of support vector regression and extreme gradient boosting for decomposition-based data-driven 10-day streamflow forecasting. J Hydrol 582:124293. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124293","journal-title":"J Hydrol"},{"key":"1524_CR53","doi-asserted-by":"publisher","first-page":"111492","DOI":"10.1016\/j.ymssp.2024.111492","volume":"216","author":"M Yu","year":"2024","unstructured":"Yu M, Zhu L, Ning J, Yang Z, Jiang Z, Xu L, Wang Y, Meng G, Huang Y (2024) Machine vision and novel attention mechanism TCN for enhanced prediction of future deposition height in directed energy deposition. Mech Syst Signal Process 216:111492. https:\/\/doi.org\/10.1016\/j.ymssp.2024.111492","journal-title":"Mech Syst Signal Process"},{"key":"1524_CR54","doi-asserted-by":"publisher","first-page":"128762","DOI":"10.1016\/j.energy.2023.128762","volume":"285","author":"D Zhang","year":"2023","unstructured":"Zhang D, Chen B, Zhu H, Goh HH, Dong Y, Wu T (2023) Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model. Energy 285:128762. https:\/\/doi.org\/10.1016\/j.energy.2023.128762","journal-title":"Energy"},{"key":"1524_CR55","doi-asserted-by":"publisher","first-page":"106090","DOI":"10.1016\/j.bspc.2024.106090","volume":"91","author":"F Zhao","year":"2024","unstructured":"Zhao F, Feng F, Ye S, Mao Y, Chen X, Li Y, Ning M, Zhang M (2024) Multi-head self-attention mechanism-based global feature learning model for ASD diagnosis. Biomed Signal Process Control 91:106090. https:\/\/doi.org\/10.1016\/j.bspc.2024.106090","journal-title":"Biomed Signal Process Control"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01524-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01524-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01524-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T08:07:33Z","timestamp":1745654853000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01524-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,12]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1524"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01524-y","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,12]]},"assertion":[{"value":"27 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 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":"All authors approved the final manuscript and the submission to this journal.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"31"}}