{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T02:54:26Z","timestamp":1773024866724,"version":"3.50.1"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The support of 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\/s12145-024-01544-8","type":"journal-article","created":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T16:13:39Z","timestamp":1735402419000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A stacking ensemble machine learning model for improving monthly 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":[]},{"given":"Miao","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Zong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yang-hao","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Hong-fei","family":"Zang","sequence":"additional","affiliation":[]},{"given":"Dong-mei","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,28]]},"reference":[{"key":"1544_CR1","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.jhydrol.2018.06.022","volume":"563","author":"JO Akanegbu","year":"2018","unstructured":"Akanegbu JO, Meri\u00f6 L-J, Marttila H, Ronkanen A-K, Kl\u00f8ve B (2018) A simple model structure enhances parameter identification and improves runoff prediction in ungauged high-latitude catchments. J Hydrol 563:395\u2013410. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.06.022","journal-title":"J Hydrol"},{"key":"1544_CR2","doi-asserted-by":"publisher","first-page":"130804","DOI":"10.1016\/j.jhydrol.2024.130804","volume":"631","author":"A Amini","year":"2024","unstructured":"Amini A, Dolatshahi M, Kerachian R (2024) Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: comparing different deep learning models. J Hydrol 631:130804. https:\/\/doi.org\/10.1016\/j.jhydrol.2024.130804","journal-title":"J Hydrol"},{"key":"1544_CR3","doi-asserted-by":"crossref","unstructured":"Bernhard S, John P, Thomas H (2007) Greedy Layer-wise training of deep networks. in: advances in neural information processing systems 19: proceedings of the 2006 Conference. MIT Press, pp 153\u2013160. doi:http:\/\/ieeexplore.ieee.org\/document\/6287632","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"1544_CR4","doi-asserted-by":"publisher","first-page":"130091","DOI":"10.1016\/j.jhydrol.2023.130091","volume":"625","author":"L Bian","year":"2023","unstructured":"Bian L, Qin X, Zhang C, Guo P, Wu H (2023) Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM-a case study for runoff simulation in an arid area. J Hydrol 625:130091. https:\/\/doi.org\/10.1016\/j.jhydrol.2023.130091","journal-title":"J Hydrol"},{"key":"1544_CR5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik VN (1995) Support-vector networks. Mach Learn 20:273\u2013297. https:\/\/doi.org\/10.1023\/A:1022627411411","journal-title":"Mach Learn"},{"key":"1544_CR6","doi-asserted-by":"publisher","first-page":"127124","DOI":"10.1016\/j.jhydrol.2021.127124","volume":"603","author":"Z Cui","year":"2021","unstructured":"Cui Z, Qing X, Chai H, Yang S, Zhu Y, Wang F (2021) Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis. J Hydrol 603:127124. https:\/\/doi.org\/10.1016\/j.jhydrol.2021.127124","journal-title":"J Hydrol"},{"key":"1544_CR7","doi-asserted-by":"publisher","first-page":"972","DOI":"10.2166\/hydro.2024.210","volume":"26","author":"D Deb","year":"2024","unstructured":"Deb D, Arunachalam V, Raju KS (2024) Daily reservoir inflow prediction using stacking ensemble of machine learning algorithms. J Hydroinformatics 26:972\u2013997. https:\/\/doi.org\/10.2166\/hydro.2024.210","journal-title":"J Hydroinformatics"},{"key":"1544_CR8","doi-asserted-by":"publisher","first-page":"101941","DOI":"10.1016\/j.asej.2022.101941","volume":"14","author":"P Ditthakit","year":"2023","unstructured":"Ditthakit P, Pinthong S, Salaeh N, Weekaew J, Thanh Tran T, Bao Pham Q (2023) Comparative study of machine learning methods and GR2M model for monthly runoff prediction. Ain Shams Eng J 14:101941. https:\/\/doi.org\/10.1016\/j.asej.2022.101941","journal-title":"Ain Shams Eng J"},{"key":"1544_CR9","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.neucom.2021.07.084","volume":"462","author":"Y Dong","year":"2021","unstructured":"Dong Y, Zhang H, Wang C, Zhou X (2021) Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm. Neurocomputing 462:169\u2013184. https:\/\/doi.org\/10.1016\/j.neucom.2021.07.084","journal-title":"Neurocomputing"},{"key":"1544_CR10","doi-asserted-by":"publisher","first-page":"122248","DOI":"10.1016\/j.jclepro.2020.122248","volume":"270","author":"H Du","year":"2020","unstructured":"Du H, Song D, Chen Z, Shu H, Guo Z (2020) Prediction model oriented for landslide displacement with step-like curve by applying ensemble empirical mode decomposition and the PSO-ELM method. J Clean Prod 270:122248. https:\/\/doi.org\/10.1016\/j.jclepro.2020.122248","journal-title":"J Clean Prod"},{"key":"1544_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-024-06893-7","author":"B Du","year":"2024","unstructured":"Du B, Wang M, Zhang J, Chen Y, Wang T (2024) Urban flood prediction based on PCSWMM and stacking integrated learning model. Nat Hazards. https:\/\/doi.org\/10.1007\/s11069-024-06893-7","journal-title":"Nat Hazards"},{"key":"1544_CR12","doi-asserted-by":"publisher","first-page":"101013","DOI":"10.1016\/j.rineng.2023.101013","volume":"18","author":"X Duan","year":"2023","unstructured":"Duan X (2023) Research on prediction of slope displacement based on a weighted combination forecasting model. Results Eng 18:101013. https:\/\/doi.org\/10.1016\/j.rineng.2023.101013","journal-title":"Results Eng"},{"key":"1544_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jer.2023.10.001","author":"T Emmanuel","year":"2023","unstructured":"Emmanuel T, Mpoeleng D, Maupong T (2023) Power plant induced-draft fan fault prediction using machine learning stacking ensemble. J Eng Res. https:\/\/doi.org\/10.1016\/j.jer.2023.10.001","journal-title":"J Eng Res"},{"key":"1544_CR14","doi-asserted-by":"publisher","first-page":"110187","DOI":"10.1016\/j.est.2023.110187","volume":"78","author":"M Fang","year":"2024","unstructured":"Fang M, Zhang F, Yang Y, Tao R, Xiao R, Zhu D (2024) The influence of optimization algorithm on the signal prediction accuracy of VMD-LSTM for the pumped storage hydropower unit. J Energy Storage 78:110187. https:\/\/doi.org\/10.1016\/j.est.2023.110187","journal-title":"J Energy Storage"},{"key":"1544_CR15","doi-asserted-by":"publisher","first-page":"124627","DOI":"10.1016\/j.jhydrol.2020.124627","volume":"583","author":"Z-k Feng","year":"2020","unstructured":"Feng Z-k, Niu W-j, Tang Z-y, Jiang Z-q, Xu Y, Liu Y, Zhang H-r (2020) Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. J Hydrol 583:124627. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.124627","journal-title":"J Hydrol"},{"key":"1544_CR16","doi-asserted-by":"publisher","first-page":"125188","DOI":"10.1016\/j.jhydrol.2020.125188","volume":"589","author":"S Gao","year":"2020","unstructured":"Gao S, Huang Y, Zhang S, Han J, Wang G, Zhang M, Lin Q (2020) Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. J Hydrol 589:125188. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.125188","journal-title":"J Hydrol"},{"key":"1544_CR17","doi-asserted-by":"publisher","first-page":"129969","DOI":"10.1016\/j.jhydrol.2023.129969","volume":"624","author":"J Guo","year":"2023","unstructured":"Guo J, Liu Y, Zou Q, Ye L, Zhu S, Zhang H (2023) Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM. J Hydrol 624:129969. https:\/\/doi.org\/10.1016\/j.jhydrol.2023.129969","journal-title":"J Hydrol"},{"key":"1544_CR18","doi-asserted-by":"publisher","first-page":"115383","DOI":"10.1016\/j.measurement.2024.115383","volume":"239","author":"Y Guo","year":"2025","unstructured":"Guo Y, Chang Y, Lu B (2025) A review of temperature prediction methods for oil-immersed transformers. Measurement 239:115383. https:\/\/doi.org\/10.1016\/j.measurement.2024.115383","journal-title":"Measurement"},{"key":"1544_CR19","doi-asserted-by":"publisher","first-page":"127653","DOI":"10.1016\/j.jhydrol.2022.127653","volume":"608","author":"H Han","year":"2022","unstructured":"Han H, Morrison RR (2022) Improved runoff forecasting performance through error predictions using a deep-learning approach. J Hydrol 608:127653. https:\/\/doi.org\/10.1016\/j.jhydrol.2022.127653","journal-title":"J Hydrol"},{"key":"1544_CR20","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"1544_CR21","doi-asserted-by":"publisher","first-page":"107123","DOI":"10.1016\/j.ijepes.2021.107123","volume":"131","author":"H Hou","year":"2021","unstructured":"Hou H, Chen X, Li M, Zhu L, Huang Y, Yu J (2021) Prediction of user outage under typhoon disaster based on multi-algorithm stacking integration. Int J Electr Power Energy Syst 131:107123. https:\/\/doi.org\/10.1016\/j.ijepes.2021.107123","journal-title":"Int J Electr Power Energy Syst"},{"key":"1544_CR22","doi-asserted-by":"publisher","first-page":"109398","DOI":"10.1016\/j.ress.2023.109398","volume":"237","author":"H Hou","year":"2023","unstructured":"Hou H, Liu C, Wei R, He H, Wang L, Li W (2023) Outage duration prediction under typhoon disaster with stacking ensemble learning. Reliab Eng Syst Saf 237:109398. https:\/\/doi.org\/10.1016\/j.ress.2023.109398","journal-title":"Reliab Eng Syst Saf"},{"key":"1544_CR23","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.egyr.2022.02.195","volume":"8","author":"L Jiang","year":"2022","unstructured":"Jiang L, Yan C, Zhang X, Zhou B, Cheng T, Zhao J, Gu J (2022) Temperature prediction of battery energy storage plant based on EGA-BiLSTM. Energy Rep 8:1009\u20131018. https:\/\/doi.org\/10.1016\/j.egyr.2022.02.195","journal-title":"Energy Rep"},{"key":"1544_CR24","doi-asserted-by":"publisher","first-page":"101549","DOI":"10.1016\/j.ejrh.2023.101549","volume":"50","author":"X Jing","year":"2023","unstructured":"Jing X, Luo J, Zuo G, Yang X (2023) Interpreting runoff forecasting of long short-term memory network: an investigation using the integrated gradient method on runoff data from the Han River Basin. J Hydrology: Reg Stud 50:101549. https:\/\/doi.org\/10.1016\/j.ejrh.2023.101549","journal-title":"J Hydrology: Reg Stud"},{"key":"1544_CR25","doi-asserted-by":"publisher","first-page":"113699","DOI":"10.1016\/j.oceaneng.2023.113699","volume":"271","author":"C J\u00f6rges","year":"2023","unstructured":"J\u00f6rges C, Berkenbrink C, Gottschalk H, Stumpe B (2023) Spatial ocean wave height prediction with CNN mixed-data deep neural networks using random field simulated bathymetry. Ocean Eng 271:113699. https:\/\/doi.org\/10.1016\/j.oceaneng.2023.113699","journal-title":"Ocean Eng"},{"key":"1544_CR26","doi-asserted-by":"publisher","first-page":"28291","DOI":"10.1007\/s10489-023-05005-5","volume":"53","author":"V Kiran Kumar","year":"2023","unstructured":"Kiran Kumar V, Ramesh KV, Rakesh V (2023) Optimizing LSTM and Bi-LSTM models for crop yield prediction and comparison of their performance with traditional machine learning techniques. Appl Intell 53:28291\u201328309. https:\/\/doi.org\/10.1007\/s10489-023-05005-5","journal-title":"Appl Intell"},{"key":"1544_CR27","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"1544_CR28","doi-asserted-by":"publisher","first-page":"135279","DOI":"10.1016\/j.jclepro.2022.135279","volume":"382","author":"Q Li","year":"2023","unstructured":"Li Q, Song Z (2023) Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model. J Clean Prod 382:135279. https:\/\/doi.org\/10.1016\/j.jclepro.2022.135279","journal-title":"J Clean Prod"},{"key":"1544_CR29","doi-asserted-by":"publisher","first-page":"107961","DOI":"10.1016\/j.asoc.2021.107961","volume":"113","author":"M Li","year":"2021","unstructured":"Li M, Yan C, Liu W (2021) The network loan risk prediction model based on convolutional neural network and stacking fusion model. Appl Soft Comput 113:107961. https:\/\/doi.org\/10.1016\/j.asoc.2021.107961","journal-title":"Appl Soft Comput"},{"key":"1544_CR30","doi-asserted-by":"publisher","first-page":"102416","DOI":"10.1016\/j.geothermics.2022.102416","volume":"103","author":"K-Q Li","year":"2022","unstructured":"Li K-Q, Kang Q, Nie J-Y, Huang X-W (2022) Artificial neural network for predicting the thermal conductivity of soils based on a systematic database. Geothermics 103:102416. https:\/\/doi.org\/10.1016\/j.geothermics.2022.102416","journal-title":"Geothermics"},{"key":"1544_CR31","doi-asserted-by":"publisher","first-page":"128410","DOI":"10.1016\/j.physa.2022.128410","volume":"610","author":"Q Li","year":"2023","unstructured":"Li Q, Cheng R, Ge H (2023) Short-term vehicle speed prediction based on BiLSTM-GRU model considering driver heterogeneity. Physica A 610:128410. https:\/\/doi.org\/10.1016\/j.physa.2022.128410","journal-title":"Physica A"},{"key":"1544_CR32","doi-asserted-by":"publisher","first-page":"100465","DOI":"10.1016\/j.dibe.2024.100465","volume":"18","author":"G Li","year":"2024","unstructured":"Li G, Wang Y, Xu C, Wang J, Fang X, Xiong C (2024a) BO-STA-LSTM: building energy prediction based on a bayesian optimized spatial-temporal attention enhanced LSTM method. Dev Built Environ 18:100465. https:\/\/doi.org\/10.1016\/j.dibe.2024.100465","journal-title":"Dev Built Environ"},{"key":"1544_CR33","doi-asserted-by":"publisher","first-page":"113911","DOI":"10.1016\/j.measurement.2023.113911","volume":"224","author":"J Li","year":"2024","unstructured":"Li J, Yang Z, Zhao Y, Yu K (2024b) SERS combined with the SAE-CNN model for estimating apple rootstocks under heavy metal copper stress. Measurement 224:113911. https:\/\/doi.org\/10.1016\/j.measurement.2023.113911","journal-title":"Measurement"},{"key":"1544_CR34","doi-asserted-by":"publisher","first-page":"e25028","DOI":"10.1016\/j.heliyon.2024.e25028","volume":"10","author":"Z Li","year":"2024","unstructured":"Li Z, Wang G, Lin D, Mashhadi A (2024c) Hybrid approach for accurate water demand prediction using socio-economic and climatic factors with ELM optimization. Heliyon 10:e25028. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25028","journal-title":"Heliyon"},{"key":"1544_CR35","doi-asserted-by":"publisher","first-page":"113643","DOI":"10.1016\/j.measurement.2023.113643","volume":"222","author":"Z Liu","year":"2023","unstructured":"Liu Z, Liu H (2023) A novel hybrid model based on GA-VMD, sample entropy reconstruction and BiLSTM for wind speed prediction. Measurement 222:113643. https:\/\/doi.org\/10.1016\/j.measurement.2023.113643","journal-title":"Measurement"},{"key":"1544_CR36","doi-asserted-by":"publisher","first-page":"126223","DOI":"10.1016\/j.jhydrol.2021.126223","volume":"598","author":"Y Liu","year":"2021","unstructured":"Liu Y, Ji Y, Liu D, Fu Q, Li T, Hou R, Li Q (2021a) A new method for runoff prediction error correction based on LS-SVM and a 4D copula joint distribution. J Hydrol 598:126223. https:\/\/doi.org\/10.1016\/j.jhydrol.2021.126223","journal-title":"J Hydrol"},{"key":"1544_CR37","doi-asserted-by":"crossref","unstructured":"Liu Y, Wu Y, Su L, Li W, Lei J (2021b) Stacking-Based ensemble learning method for house price prediction. in proceedings of the computational methods in systems and Software . Cham: Springer International Publishing,\u00a0pp. 224\u2013237","DOI":"10.1007\/978-3-030-90318-3_22"},{"key":"1544_CR38","doi-asserted-by":"publisher","first-page":"127762","DOI":"10.1016\/j.jhydrol.2022.127762","volume":"609","author":"G Liu","year":"2022","unstructured":"Liu G, Tang Z, Qin H, Liu S, Shen Q, Qu Y, Zhou J (2022) Short-term runoff prediction using deep learning multi-dimensional ensemble method. J Hydrol 609:127762. https:\/\/doi.org\/10.1016\/j.jhydrol.2022.127762","journal-title":"J Hydrol"},{"key":"1544_CR39","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s42243-023-01097-y","volume":"31","author":"X-j Liu","year":"2024","unstructured":"Liu X-j, Zhang Y-j, Li X, Zhang Z-f, Li H-y, Liu R, Chen S-j (2024) Prediction for permeability index of blast furnace based on VMD\u2013PSO\u2013BP model. J Iron Steel Res Int 31:573\u2013583. https:\/\/doi.org\/10.1007\/s42243-023-01097-y","journal-title":"J Iron Steel Res Int"},{"key":"1544_CR40","doi-asserted-by":"publisher","first-page":"101761","DOI":"10.1016\/j.apr.2023.101761","volume":"14","author":"J Luo","year":"2023","unstructured":"Luo J, Gong Y (2023) Air pollutant prediction based on ARIMA-WOA-LSTM model. Atmos Pollut Res 14:101761. https:\/\/doi.org\/10.1016\/j.apr.2023.101761","journal-title":"Atmos Pollut Res"},{"key":"1544_CR41","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1631\/2023.A2200297","volume":"23","author":"F Lv","year":"2022","unstructured":"Lv F, Yu J, Zhang J, Yu P, Tong D-w, Wu B-P (2022) A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation. J Zhejiang University-SCIENCE A 23:1027\u20131046. https:\/\/doi.org\/10.1631\/2023.A2200297","journal-title":"J Zhejiang University-SCIENCE A"},{"key":"1544_CR42","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.jhydrol.2018.11.015","volume":"568","author":"E Meng","year":"2019","unstructured":"Meng E, Huang S, Huang Q, Fang W, Wu L, Wang L (2019) A robust method for non-stationary streamflow prediction based on improved EMD-SVM model. J Hydrol 568:462\u2013478. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.11.015","journal-title":"J Hydrol"},{"key":"1544_CR43","doi-asserted-by":"publisher","first-page":"112230","DOI":"10.1016\/j.measurement.2022.112230","volume":"205","author":"R Muhammad Adnan Ikram","year":"2022","unstructured":"Muhammad Adnan Ikram R, Dai H-L, mirshekari chargari M, Al-Bahrani M, Mamlooki M (2022) Prediction of the FRP reinforced concrete beam shear capacity by using ELM-CRFOA. Measurement 205:112230. https:\/\/doi.org\/10.1016\/j.measurement.2022.112230","journal-title":"Measurement"},{"key":"1544_CR44","doi-asserted-by":"publisher","first-page":"108182","DOI":"10.1016\/j.petrol.2020.108182","volume":"200","author":"DA Otchere","year":"2021","unstructured":"Otchere DA, Arbi Ganat TO, Gholami R, Ridha S (2021) Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: comparative analysis of ANN and SVM models. J Petrol Sci Eng 200:108182. https:\/\/doi.org\/10.1016\/j.petrol.2020.108182","journal-title":"J Petrol Sci Eng"},{"key":"1544_CR45","doi-asserted-by":"publisher","first-page":"050021","DOI":"10.1063\/5.0158972","volume":"2821","author":"N Prasad","year":"2023","unstructured":"Prasad N, Pramila PV (2023) Prediction of human activity recognition using decision tree algorithm in comparison of grid search algorithm accuracy. AIP Conf Proc 2821:050021. https:\/\/doi.org\/10.1063\/5.0158972","journal-title":"AIP Conf Proc"},{"key":"#cr-split#-1544_CR46.1","doi-asserted-by":"crossref","unstructured":"Purnama IPC, Ginantra NLWSR, Darma IWAS, Udayana IPAED (2023) Comparison of Support Vector Machine (SVM) and Linear Regression","DOI":"10.1109\/ICIC60109.2023.10381982"},{"key":"#cr-split#-1544_CR46.2","unstructured":"(LR) for stock price prediction. In: 2023 Eighth International Conference on Informatics and Computing (ICIC). Manado, Indonesia, IEEE,\u00a0pp 1-6"},{"key":"1544_CR47","doi-asserted-by":"publisher","first-page":"121216","DOI":"10.1016\/j.energy.2021.121216","volume":"235","author":"W Qiao","year":"2021","unstructured":"Qiao W, Liu W, Liu E (2021) A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S. Energy 235:121216. https:\/\/doi.org\/10.1016\/j.energy.2021.121216","journal-title":"Energy"},{"key":"1544_CR48","doi-asserted-by":"publisher","first-page":"120616","DOI":"10.1016\/j.eswa.2023.120616","volume":"229","author":"X Qiao","year":"2023","unstructured":"Qiao X, Peng T, Sun N, Zhang C, Liu Q, Zhang Y, Wang Y (2023) Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation and multi-step runoff prediction. Expert Syst Appl 229:120616. https:\/\/doi.org\/10.1016\/j.eswa.2023.120616","journal-title":"Expert Syst Appl"},{"key":"1544_CR49","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.neucom.2019.04.061","volume":"356","author":"L Qin","year":"2019","unstructured":"Qin L, Li W, Li S (2019) Effective passenger flow forecasting using STL and ESN based on two improvement strategies. Neurocomputing 356:244\u2013256. https:\/\/doi.org\/10.1016\/j.neucom.2019.04.061","journal-title":"Neurocomputing"},{"key":"1544_CR50","doi-asserted-by":"publisher","first-page":"108535","DOI":"10.1016\/j.asoc.2022.108535","volume":"118","author":"M Querales","year":"2022","unstructured":"Querales M, Salas R, Morales Y, Allende-Cid H, Rosas H (2022) A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations. Appl Soft Comput 118:108535. https:\/\/doi.org\/10.1016\/j.asoc.2022.108535","journal-title":"Appl Soft Comput"},{"key":"1544_CR51","doi-asserted-by":"publisher","first-page":"108838","DOI":"10.1016\/j.petrol.2021.108838","volume":"205","author":"L Shan","year":"2021","unstructured":"Shan L, Liu Y, Tang M, Yang M, Bai X (2021) CNN-BiLSTM hybrid neural networks with attention mechanism for well log prediction. J Petrol Sci Eng 205:108838. https:\/\/doi.org\/10.1016\/j.petrol.2021.108838","journal-title":"J Petrol Sci Eng"},{"key":"1544_CR52","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.iswcr.2022.09.001","volume":"11","author":"W Shi","year":"2023","unstructured":"Shi W, Wang M, Li D, Li X, Sun M (2023) An improved method that incorporates the estimated runoff for peak discharge prediction on the Chinese Loess Plateau. Int Soil Water Conserv Res 11:290\u2013300. https:\/\/doi.org\/10.1016\/j.iswcr.2022.09.001","journal-title":"Int Soil Water Conserv Res"},{"key":"1544_CR53","doi-asserted-by":"publisher","first-page":"127324","DOI":"10.1016\/j.jhydrol.2021.127324","volume":"605","author":"CM Song","year":"2022","unstructured":"Song CM (2022) Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability. J Hydrol 605:127324. https:\/\/doi.org\/10.1016\/j.jhydrol.2021.127324","journal-title":"J Hydrol"},{"key":"1544_CR54","doi-asserted-by":"publisher","first-page":"113642","DOI":"10.1016\/j.enbuild.2023.113642","volume":"300","author":"Y Song","year":"2023","unstructured":"Song Y, Xie H, Zhu Z, Ji R (2023) Predicting energy consumption of chiller plant using WOA-BiLSTM hybrid prediction model: a case study for a hospital building. Energy Build 300:113642. https:\/\/doi.org\/10.1016\/j.enbuild.2023.113642","journal-title":"Energy Build"},{"key":"1544_CR55","doi-asserted-by":"publisher","first-page":"5633","DOI":"10.1007\/s00500-020-05560-w","volume":"25","author":"Y Sun","year":"2021","unstructured":"Sun Y, Ding S, Zhang Z, Jia W (2021) An improved grid search algorithm to optimize SVR for prediction. Soft Comput 25:5633\u20135644. https:\/\/doi.org\/10.1007\/s00500-020-05560-w","journal-title":"Soft Comput"},{"key":"1544_CR56","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.2166\/hydro.2013.134","volume":"15","author":"W-c Wang","year":"2013","unstructured":"Wang W-c, Xu D-m, Chau K-w, Chen S (2013) Improved annual rainfall-runoff forecasting using PSO\u2013SVM model based on EEMD. J Hydroinformatics 15:1377\u20131390. https:\/\/doi.org\/10.2166\/hydro.2013.134","journal-title":"J Hydroinformatics"},{"key":"1544_CR57","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1007\/s11269-021-02920-5","volume":"35","author":"W Wang","year":"2021","unstructured":"Wang W, Du Y-j, Chau K-w, Xu D-m, Liu C, 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":"1544_CR58","doi-asserted-by":"publisher","first-page":"115027","DOI":"10.1016\/j.microrel.2023.115027","volume":"146","author":"F Wang","year":"2023","unstructured":"Wang F, Yang Y, Huang T, Xu Y (2023a) Lifetime prediction of electronic devices based on the P-stacking machine learning model. Microelectron Reliab 146:115027. https:\/\/doi.org\/10.1016\/j.microrel.2023.115027","journal-title":"Microelectron Reliab"},{"key":"1544_CR59","doi-asserted-by":"publisher","first-page":"128910","DOI":"10.1016\/j.energy.2023.128910","volume":"282","author":"K Wang","year":"2023","unstructured":"Wang K, Hua Y, Huang L, Guo X, Liu X, Ma Z, Ma R (2023b) A novel GA-LSTM-based prediction method of ship energy usage based on the characteristics analysis of operational data. Energy 282:128910. https:\/\/doi.org\/10.1016\/j.energy.2023.128910","journal-title":"Energy"},{"key":"1544_CR60","doi-asserted-by":"publisher","first-page":"129460","DOI":"10.1016\/j.jhydrol.2023.129460","volume":"620","author":"W-c Wang","year":"2023","unstructured":"Wang W-c, Cheng Q, Chau K-w, Hu H, Zang H-f, Xu D-m (2023c) An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition. J Hydrol 620:129460. https:\/\/doi.org\/10.1016\/j.jhydrol.2023.129460","journal-title":"J Hydrol"},{"key":"1544_CR61","doi-asserted-by":"publisher","first-page":"2373","DOI":"10.1007\/s12145-023-01038-z","volume":"16","author":"W-c Wang","year":"2023","unstructured":"Wang W-c, Wang B, Chau K-w, Xu D-m (2023d) Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation. Earth Sci Inf 16:2373\u20132389. https:\/\/doi.org\/10.1007\/s12145-023-01038-z","journal-title":"Earth Sci Inf"},{"key":"1544_CR62","doi-asserted-by":"publisher","first-page":"129753","DOI":"10.1016\/j.energy.2023.129753","volume":"288","author":"S Wang","year":"2024","unstructured":"Wang S, Shi J, Yang W, Yin Q (2024) High and low frequency wind power prediction based on transformer and BiGRU-attention. Energy 288:129753. https:\/\/doi.org\/10.1016\/j.energy.2023.129753","journal-title":"Energy"},{"key":"1544_CR63","doi-asserted-by":"publisher","first-page":"103558","DOI":"10.1016\/j.est.2021.103558","volume":"47","author":"M Wei","year":"2022","unstructured":"Wei M, Ye M, Wang Q, Xinxin X, Twajamahoro JP (2022) Remaining useful life prediction of lithium-ion batteries based on stacked autoencoder and gaussian mixture regression. J Energy Storage 47:103558. https:\/\/doi.org\/10.1016\/j.est.2021.103558","journal-title":"J Energy Storage"},{"key":"1544_CR64","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.jhydrol.2018.12.060","volume":"570","author":"X Wen","year":"2019","unstructured":"Wen X, Feng Q, Deo RC, Wu M, Yin Z, Yang L, Singh VP (2019) Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems. J Hydrol 570:167\u2013184. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.12.060","journal-title":"J Hydrol"},{"key":"1544_CR65","doi-asserted-by":"publisher","first-page":"128048","DOI":"10.1016\/j.energy.2023.128048","volume":"279","author":"S Wen","year":"2023","unstructured":"Wen S, Wang H, Qian J, Men X (2023) A novel combined model based on echo state network optimized by whale optimization algorithm for blast furnace gas prediction. Energy 279:128048. https:\/\/doi.org\/10.1016\/j.energy.2023.128048","journal-title":"Energy"},{"key":"1544_CR66","doi-asserted-by":"publisher","first-page":"101966","DOI":"10.1016\/j.inffus.2023.101966","volume":"100","author":"X Wu","year":"2023","unstructured":"Wu X, Zhan J, Ding W (2023) TWC-EL: a multivariate prediction model by the fusion of three-way clustering and ensemble learning. Inform Fusion 100:101966. https:\/\/doi.org\/10.1016\/j.inffus.2023.101966","journal-title":"Inform Fusion"},{"key":"1544_CR67","doi-asserted-by":"publisher","first-page":"123915","DOI":"10.1016\/j.jhydrol.2019.123915","volume":"577","author":"T Xie","year":"2019","unstructured":"Xie T, Zhang G, Hou J, Xie J, Lv M, Liu F (2019) Hybrid forecasting model for non-stationary daily runoff series: a case study in the Han River Basin, China. J Hydrol 577:123915. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123915","journal-title":"J Hydrol"},{"key":"1544_CR68","doi-asserted-by":"publisher","first-page":"119469","DOI":"10.1016\/j.eswa.2022.119469","volume":"217","author":"Y Xie","year":"2023","unstructured":"Xie Y, Sun W, Ren M, Chen S, Huang Z, Pan X (2023) Stacking ensemble learning models for daily runoff prediction using 1D and 2D CNNs. Expert Syst Appl 217:119469. https:\/\/doi.org\/10.1016\/j.eswa.2022.119469","journal-title":"Expert Syst Appl"},{"key":"1544_CR69","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 Hydroinformatics 25:943\u2013970. https:\/\/doi.org\/10.2166\/hydro.2023.172","journal-title":"J Hydroinformatics"},{"key":"1544_CR70","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 (2024) 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":"1544_CR71","doi-asserted-by":"publisher","DOI":"10.1007\/s00202-024-02676-2","author":"Q Yang","year":"2024","unstructured":"Yang Q, Duan J, Bian H, Zhang B (2024a) Equivalent inertia prediction for power systems with virtual inertia based on PSO-SVM. Electr Eng. https:\/\/doi.org\/10.1007\/s00202-024-02676-2","journal-title":"Electr Eng"},{"key":"1544_CR72","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s11269-023-03668-w","volume":"38","author":"X Yang","year":"2024","unstructured":"Yang X, Chen Z, Qin M (2024b) Monthly runoff prediction via mode decomposition-recombination technique. Water Resour Manage 38:269\u2013286. https:\/\/doi.org\/10.1007\/s11269-023-03668-w","journal-title":"Water Resour Manage"},{"key":"1544_CR73","doi-asserted-by":"publisher","first-page":"110890","DOI":"10.1016\/j.anucene.2024.110890","volume":"210","author":"L Yang","year":"2025","unstructured":"Yang L et al (2025) LSTM-GCN based multidimensional parameter relationship analysis and prediction framework for system level experimental bench. Ann Nucl Energy 210:110890. https:\/\/doi.org\/10.1016\/j.anucene.2024.110890","journal-title":"Ann Nucl Energy"},{"key":"1544_CR74","doi-asserted-by":"publisher","first-page":"109166","DOI":"10.1016\/j.measurement.2021.109166","volume":"175","author":"D Yao","year":"2021","unstructured":"Yao D, Li B, Liu H, Yang J, Jia L (2021) Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit. Measurement 175:109166. https:\/\/doi.org\/10.1016\/j.measurement.2021.109166","journal-title":"Measurement"},{"key":"1544_CR75","doi-asserted-by":"publisher","first-page":"4135","DOI":"10.1007\/s12206-024-0710-z","volume":"38","author":"X Yin","year":"2024","unstructured":"Yin X, Zhang S, Zhang Y, Pang Z, Zhang B (2024) Friction performance prediction of automotive pads under operating conditions using attention-based CNN-BiLSTM deep learning framework. J Mech Sci Technol 38:4135\u20134144. https:\/\/doi.org\/10.1007\/s12206-024-0710-z","journal-title":"J Mech Sci Technol"},{"key":"1544_CR76","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/s11676-017-0448-x","volume":"29","author":"J Zhang","year":"2018","unstructured":"Zhang J, Song W, Jiang B, Li M (2018) Measurement of lumber moisture content based on PCA and GS-SVM. J Forestry Res 29:557\u2013564. https:\/\/doi.org\/10.1007\/s11676-017-0448-x","journal-title":"J Forestry Res"},{"key":"1544_CR77","doi-asserted-by":"publisher","first-page":"105857","DOI":"10.1016\/j.cnsns.2021.105857","volume":"101","author":"H Zhang","year":"2021","unstructured":"Zhang H, Zheng M, Zhang Y, Yu X, Li W, Gao H (2021) Application of ESN prediction model based on compressed sensing in stock market. Commun Nonlinear Sci Numer Simul 101:105857. https:\/\/doi.org\/10.1016\/j.cnsns.2021.105857","journal-title":"Commun Nonlinear Sci Numer Simul"},{"key":"1544_CR78","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1007\/s12145-022-00819-2","volume":"15","author":"X Zhang","year":"2022","unstructured":"Zhang X, Wang K, Zheng Z (2022) A novel integrated learning model for rainfall prediction CEEMD- FCMSE -Stacking. Earth Sci Inf 15:1995\u20132005. https:\/\/doi.org\/10.1007\/s12145-022-00819-2","journal-title":"Earth Sci Inf"},{"key":"1544_CR79","doi-asserted-by":"publisher","first-page":"115338","DOI":"10.1016\/j.oceaneng.2023.115338","volume":"285","author":"J Zhang","year":"2023","unstructured":"Zhang J, Xin X, Shang Y, Wang Y, Zhang L (2023a) Nonstationary significant wave height forecasting with a hybrid VMD-CNN model. Ocean Eng 285:115338. https:\/\/doi.org\/10.1016\/j.oceaneng.2023.115338","journal-title":"Ocean Eng"},{"key":"1544_CR80","doi-asserted-by":"publisher","unstructured":"Zhang J, Ye L, Lai Y (2023b) Stock Price Prediction using CNN-BiLSTM-Attention model. Mathematics 11. https:\/\/doi.org\/10.3390\/math11091985","DOI":"10.3390\/math11091985"},{"key":"1544_CR81","doi-asserted-by":"publisher","first-page":"110873","DOI":"10.1016\/j.asoc.2023.110873","volume":"148","author":"S Zhang","year":"2023","unstructured":"Zhang S, Liu M, Liu M, Lei Z, Zeng G, Chen Z (2023c) Day-ahead wind power prediction using an ensemble model considering multiple indicators combined with error correction. Appl Soft Comput 148:110873. https:\/\/doi.org\/10.1016\/j.asoc.2023.110873","journal-title":"Appl Soft Comput"},{"key":"1544_CR82","doi-asserted-by":"publisher","first-page":"126190","DOI":"10.1016\/j.energy.2022.126190","volume":"264","author":"W Zhang","year":"2023","unstructured":"Zhang W, Zhou H, Bao X, Cui H (2023d) Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through CNN\u2013LSTM hybrid model. Energy 264:126190. https:\/\/doi.org\/10.1016\/j.energy.2022.126190","journal-title":"Energy"},{"key":"1544_CR83","doi-asserted-by":"publisher","first-page":"3221","DOI":"10.1007\/s00477-023-02446-9","volume":"37","author":"X Zhang","year":"2023","unstructured":"Zhang X, Chen H, Wen Y, Shi J, Xiao Y (2023e) A new water level prediction model based on ESMD\u2009\u2013\u2009VMD\u2009\u2013\u2009WSD\u2009\u2013\u2009ESN. Stoch Env Res Risk Assess 37:3221\u20133241. https:\/\/doi.org\/10.1007\/s00477-023-02446-9","journal-title":"Stoch Env Res Risk Assess"},{"key":"1544_CR84","doi-asserted-by":"publisher","first-page":"113527","DOI":"10.1016\/j.enbuild.2023.113527","volume":"298","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Li W, Zhang J, Jiang C, Chen S (2023) Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network. Energy Build 298:113527. https:\/\/doi.org\/10.1016\/j.enbuild.2023.113527","journal-title":"Energy Build"},{"key":"1544_CR85","doi-asserted-by":"publisher","first-page":"954","DOI":"10.2166\/hydro.2024.196","volume":"26","author":"H Zhao","year":"2024","unstructured":"Zhao H, Liao S, Song Y, Fang Z, Ma X, Zhou B (2024) Long-term inflow forecast using meteorological data based on long short-term memory neural networks. J Hydroinformatics 26:954\u2013971. https:\/\/doi.org\/10.2166\/hydro.2024.196","journal-title":"J Hydroinformatics"},{"key":"1544_CR86","doi-asserted-by":"publisher","first-page":"120223","DOI":"10.1016\/j.eswa.2023.120223","volume":"227","author":"B Zhu","year":"2023","unstructured":"Zhu B, Qian C, vanden Broucke S, Xiao J, Li Y (2023a) A bagging-based selective ensemble model for churn prediction on imbalanced data. Expert Syst Appl 227:120223. https:\/\/doi.org\/10.1016\/j.eswa.2023.120223","journal-title":"Expert Syst Appl"},{"key":"1544_CR87","doi-asserted-by":"publisher","first-page":"115306","DOI":"10.1016\/j.oceaneng.2023.115306","volume":"285","author":"H Zhu","year":"2023","unstructured":"Zhu H, Su K, Luo G (2023b) Effective foundation damping prediction of monopile-supported offshore wind turbines based on integrated fitting equation and PSO\u2013SVM algorithm. Ocean Eng 285:115306. https:\/\/doi.org\/10.1016\/j.oceaneng.2023.115306","journal-title":"Ocean Eng"},{"key":"1544_CR88","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1016\/j.egyr.2022.12.044","volume":"9","author":"Z Zhu","year":"2023","unstructured":"Zhu Z, Zhou M, Hu F, Wang S, Ma J, Gao B, Bian K (2023c) A day-ahead industrial load forecasting model using load change rate features and combining FA-ELM and the AdaBoost algorithm. Energy Rep 9:971\u2013981. https:\/\/doi.org\/10.1016\/j.egyr.2022.12.044","journal-title":"Energy Rep"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01544-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01544-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01544-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T08:05:37Z","timestamp":1745654737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01544-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,28]]},"references-count":89,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1544"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01544-8","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,28]]},"assertion":[{"value":"29 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"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":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"120"}}