{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:52:21Z","timestamp":1783540341591,"version":"3.55.0"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00521-025-11538-2","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T14:30:43Z","timestamp":1755873043000},"page":"23533-23589","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An interpretable information fusion approach to impute meteorological missing values toward cross-domain intelligent forecasting"],"prefix":"10.1007","volume":"37","author":[{"given":"Milad","family":"Zarchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zohreh Hashemi","family":"Aslani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3202-873X","authenticated-orcid":false,"given":"Kong Fah","family":"Tee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"issue":"1","key":"11538_CR1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3390\/fi17010008","volume":"17","author":"V Babaey","year":"2025","unstructured":"Babaey V, Ravindran A (2025) GenSQLi: a generative artificial intelligence framework for automatically securing web application firewalls against structured query language injection attacks. Future Internet 17(1):8","journal-title":"Future Internet"},{"key":"11538_CR2","doi-asserted-by":"crossref","unstructured":"Babaey V, Ravindran A (2025) GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs. In SoutheastCon 2025 (pp. 1519\u20131524). IEEE","DOI":"10.1109\/SoutheastCon56624.2025.10971558"},{"issue":"6","key":"11538_CR3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.3390\/computers14060205","volume":"14","author":"V Babaey","year":"2025","unstructured":"Babaey V, Faragardi HR (2025) Detecting zero-day web attacks with an ensemble of LSTM, GRU, and stacked autoencoders. Computers 14(6):205","journal-title":"Computers"},{"key":"11538_CR4","unstructured":"Mirnajafizadeh SMM, Sethuram AR, Mohaisen D, Nyang D, Jang R (2024) Enhancing network attack detection with distributed and {In-Network} data collection system. In 33rd USENIX Security Symposium (USENIX Security 24) (pp. 5161\u20135178)"},{"key":"11538_CR5","doi-asserted-by":"crossref","unstructured":"Kim S, Mirnajafizadeh SMM, Kim B, Jang R, Nyang D (2025) SketchFeature: High-quality per-flow feature extractor towards security-aware data plane. In Proc. of ISOC NDSS","DOI":"10.14722\/ndss.2025.241071"},{"key":"11538_CR6","unstructured":"Yang S, Mirnajafizadeh SMM, Kim S, Jang R, Nyang D (2025) Enhancing resiliency of sketch-based security via LSB sharing-based dynamic late merging. arXiv preprint arXiv:2503.11777"},{"issue":"2","key":"11538_CR7","doi-asserted-by":"crossref","first-page":"e12237","DOI":"10.1049\/tje2.12237","volume":"2023","author":"M Ahmadi","year":"2023","unstructured":"Ahmadi M, Rastgoo S, Mahdavi Z, Nasab MA, Zand M, Sanjeevikumar P, Khan B (2023) Optimal allocation of EVs parking lots and DG in micro grid using two-stage GA-PSO. J Eng 2023(2):e12237","journal-title":"J Eng"},{"issue":"1","key":"11538_CR8","first-page":"9921062","volume":"2024","author":"Z Mahdavi","year":"2024","unstructured":"Mahdavi Z, Samavat T, Javanmardi ASJ, Dashtaki MA, Zand M, Nasab MA, et al. (2024) Providing a control system for charging electric vehicles using ANFIS. Int Trans Electr Energy Syst 2024(1):9921062","journal-title":"Int Trans Electr Energy Syst"},{"issue":"12","key":"11538_CR9","doi-asserted-by":"crossref","first-page":"222","DOI":"10.3390\/wevj13120222","volume":"13","author":"S Rastgoo","year":"2022","unstructured":"Rastgoo S, Mahdavi Z, Azimi Nasab M, Zand M, Padmanaban S (2022) Using an intelligent control method for electric vehicle charging in microgrids. World Electr Veh J 13(12):222","journal-title":"World Electr Veh J"},{"issue":"1","key":"11538_CR10","first-page":"5749756","volume":"2023","author":"A Kermani","year":"2023","unstructured":"Kermani A, Jamshidi AM, Mahdavi Z, Dashtaki AA, Zand M, Nasab MA, et al. (2023) Energy management system for smart grid in the presence of energy storage and photovoltaic systems. Int J Photoenergy 2023(1):5749756","journal-title":"Int J Photoenergy"},{"issue":"5","key":"11538_CR11","doi-asserted-by":"crossref","first-page":"200","DOI":"10.3390\/ijgi14050200","volume":"14","author":"E Kefayat","year":"2025","unstructured":"Kefayat E, Thill JC (2025) Urban street network configuration and property crime: an empirical multivariate case study. ISPRS Int J Geo-Inf 14(5):200","journal-title":"ISPRS Int J Geo-Inf"},{"issue":"3","key":"11538_CR12","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1007\/s11277-023-10719-3","volume":"132","author":"M Moshiri","year":"2023","unstructured":"Moshiri M, Heidari AA, Ghafoorzadeh-Yazdi A (2023) Wideband two-layer transmitarray antenna based on non-uniform layers. Wirel Pers Commun 132(3):2225\u20132241","journal-title":"Wirel Pers Commun"},{"key":"11538_CR13","doi-asserted-by":"crossref","first-page":"153031","DOI":"10.1016\/j.aeue.2019.153031","volume":"115","author":"M Moshiri","year":"2020","unstructured":"Moshiri M, Ghafoorzadeh-Yazdi A, Heidari AA (2020) A wideband quad-layer transmitarray antenna with double cross slot rings elements. AEU-Int J Electron Commun 115:153031","journal-title":"AEU-Int J Electron Commun"},{"key":"11538_CR14","unstructured":"Filvantorkaman M, Piri M, FilvanTorkaman M, Zabihi A, Moradi H (2025) Fusion-based brain tumor classification using deep learning and explainable AI, and rule-based reasoning. arXiv preprint arXiv:2508.06891"},{"key":"11538_CR15","doi-asserted-by":"crossref","unstructured":"Mahdavi Z (2022) Introduce improved CNN model for accurate classification of autism spectrum disorder using 3D MRI brain scans. Proceedings of the MOL2NET 22","DOI":"10.3390\/mol2net-08-13727"},{"key":"11538_CR16","doi-asserted-by":"crossref","unstructured":"Mahdavi Z (2022) MRI Brain Tumors Detection by Proposed U-Net Model. Proceedings of the MOL2NET 22","DOI":"10.3390\/mol2net-08-13726"},{"key":"11538_CR17","unstructured":"Mohammadagha M, Naeini HK, Asadi S, Najafi M, Kasuhal V (2025) Machine learning model for condition assessment of trenchless vitrified clay pipes. In North American Society for Trenchless Technology (NASTT) 2025 No-Dig Show"},{"key":"11538_CR18","doi-asserted-by":"publisher","unstructured":"Mohammadagha M (2025) Hybridization and Optimization Modeling, Analysis, and Comparative Study of Sorting Algorithms: Adaptive Techniques, Parallelization, for Mergesort, Heapsort, Quicksort, Insertion Sort, Selection Sort, and Bubble Sort. https:\/\/doi.org\/10.31224\/4537","DOI":"10.31224\/4537"},{"key":"11538_CR19","unstructured":"Asadi S, Mohammadagha M, Naeini HK (2025) Comprehensive Review of Analytical and Numerical Approaches in Earth-to-Air Heat Exchangers and Exergoeconomic Evaluations. arXiv preprint arXiv:2502.08553"},{"key":"11538_CR20","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s00521-013-1469-9","volume":"25","author":"\u00d6 Terzi","year":"2014","unstructured":"Terzi \u00d6, Ergin G (2014) Forecasting of monthly river flow with autoregressive modeling and data-driven techniques. Neural Comput Appl 25:179\u2013188. https:\/\/doi.org\/10.1007\/s00521-013-1469-9","journal-title":"Neural Comput Appl"},{"key":"11538_CR21","doi-asserted-by":"publisher","first-page":"2417","DOI":"10.1007\/s00521-015-2012-y","volume":"27","author":"E Cadenas","year":"2016","unstructured":"Cadenas E, Rivera W, Campos-Amezcua R et al (2016) Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, M\u00e9xico. Neural Comput Appl 27:2417\u20132428. https:\/\/doi.org\/10.1007\/s00521-015-2012-y","journal-title":"Neural Comput Appl"},{"key":"11538_CR22","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s00521-018-3707-7","volume":"32","author":"L Cornejo-Bueno","year":"2020","unstructured":"Cornejo-Bueno L, Camacho-G\u00f3mez C, Aybar-Ruiz A et al (2020) Wind power ramp event detection with a hybrid neuro-evolutionary approach. Neural Comput Appl 32:391\u2013402. https:\/\/doi.org\/10.1007\/s00521-018-3707-7","journal-title":"Neural Comput Appl"},{"key":"11538_CR23","doi-asserted-by":"publisher","first-page":"15073","DOI":"10.1007\/s00521-021-06082-8","volume":"33","author":"I Yeo","year":"2021","unstructured":"Yeo I, Choi Y, Lops Y et al (2021) Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms. Neural Comput Appl 33:15073\u201315089. https:\/\/doi.org\/10.1007\/s00521-021-06082-8","journal-title":"Neural Comput Appl"},{"key":"11538_CR24","doi-asserted-by":"publisher","first-page":"13849","DOI":"10.1007\/s00521-022-07175-8","volume":"34","author":"X Cheng","year":"2022","unstructured":"Cheng X, Zhang W, Wenzel A et al (2022) Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction. Neural Comput Appl 34:13849\u201313866. https:\/\/doi.org\/10.1007\/s00521-022-07175-8","journal-title":"Neural Comput Appl"},{"key":"11538_CR25","doi-asserted-by":"publisher","first-page":"17281","DOI":"10.1007\/s00521-023-08619-5","volume":"35","author":"FM Talaat","year":"2023","unstructured":"Talaat FM (2023) Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes. Neural Comput Appl 35:17281\u201317292. https:\/\/doi.org\/10.1007\/s00521-023-08619-5","journal-title":"Neural Comput Appl"},{"key":"11538_CR26","doi-asserted-by":"publisher","first-page":"19669","DOI":"10.1007\/s00521-023-08842-0","volume":"35","author":"Ed de Santos Jesus","year":"2023","unstructured":"de Santos Jesus Ed, Silva Gomes GSd (2023) Machine learning models for forecasting water demand for the Metropolitan Region of Salvador, Bahia. Neural Comput Appl 35:19669\u201319683. https:\/\/doi.org\/10.1007\/s00521-023-08842-0","journal-title":"Neural Comput Appl"},{"key":"11538_CR27","doi-asserted-by":"publisher","first-page":"5109","DOI":"10.1007\/s00521-023-09224-2","volume":"36","author":"MF Ak","year":"2024","unstructured":"Ak MF, Demir E (2024) A novel qualitative risk assessment using the interval-valued spherical fuzzy extension of TOPSIS method: a case study in rail transit systems. Neural Comput Appl 36:5109\u20135132. https:\/\/doi.org\/10.1007\/s00521-023-09224-2","journal-title":"Neural Comput Appl"},{"key":"11538_CR28","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1007\/s00521-023-09189-2","volume":"36","author":"R Shirani Faradonbeh","year":"2024","unstructured":"Shirani Faradonbeh R, Vaisey W, Sharifzadeh M et al (2024) Hybridized intelligent multi-class classifiers for rockburst risk assessment in deep underground mines. Neural Comput Appl 36:1681\u20131698. https:\/\/doi.org\/10.1007\/s00521-023-09189-2","journal-title":"Neural Comput Appl"},{"key":"11538_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-09046-2","author":"T Dong","year":"2023","unstructured":"Dong T, Li H, Zhang Z (2023) The using effect of fuzzy analytic hierarchy process in project engineering risk management. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-023-09046-2","journal-title":"Neural Comput Appl"},{"key":"11538_CR30","doi-asserted-by":"publisher","first-page":"15241","DOI":"10.1007\/s00521-022-06959-2","volume":"34","author":"HI Kure","year":"2022","unstructured":"Kure HI, Islam S, Mouratidis H (2022) An integrated cyber security risk management framework and risk predication for the critical infrastructure protection. Neural Comput Appl 34:15241\u201315271. https:\/\/doi.org\/10.1007\/s00521-022-06959-2","journal-title":"Neural Comput Appl"},{"key":"11538_CR31","doi-asserted-by":"publisher","first-page":"4549","DOI":"10.1007\/s00521-022-07929-4","volume":"35","author":"S Jafarzadeh Ghoushchi","year":"2023","unstructured":"Jafarzadeh Ghoushchi S, Shaffiee Haghshenas S, Memarpour Ghiaci A et al (2023) Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment. Neural Comput Appl 35:4549\u20134567. https:\/\/doi.org\/10.1007\/s00521-022-07929-4","journal-title":"Neural Comput Appl"},{"key":"11538_CR32","doi-asserted-by":"publisher","first-page":"5133","DOI":"10.1007\/s00521-023-09414-y","volume":"36","author":"MF Ak","year":"2024","unstructured":"Ak MF, Demir E (2024) A novel qualitative risk assessment using the interval-valued spherical fuzzy extension of TOPSIS method: a case study in rail transit systems. Neural Comput Appl 36:5133. https:\/\/doi.org\/10.1007\/s00521-023-09414-y","journal-title":"Neural Comput Appl"},{"key":"11538_CR33","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2021.111236","volume":"151","author":"A Alamoodi","year":"2021","unstructured":"Alamoodi A, Zaidan B, Zaidan A, Albahri O, Chen J, Chyad M, Aleesa A (2021) Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation. Chaos Solitons Fractals 151:111236","journal-title":"Chaos Solitons Fractals"},{"key":"11538_CR34","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.apenergy.2018.06.087","volume":"228","author":"A Ag\u00fcera-P\u00e9rez","year":"2018","unstructured":"Ag\u00fcera-P\u00e9rez A, Palomares-Salas JC, De La Rosa JJG, Florencias-Oliveros O (2018) Weather forecasts for microgrid energy management: review, discussion and recommendations. Appl Energy 228:265\u2013278","journal-title":"Appl Energy"},{"key":"11538_CR35","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.eswa.2019.03.044","volume":"129","author":"A Andiojaya","year":"2019","unstructured":"Andiojaya A, Demirhan H (2019) A bagging algorithm for the imputation of missing values in time series. Expert Syst Appl 129:10\u201326","journal-title":"Expert Syst Appl"},{"key":"11538_CR36","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2021.126454","volume":"598","author":"P Arriagada","year":"2021","unstructured":"Arriagada P, Karelovic B, Link O (2021) Automatic gap-filling of daily streamflow time series in data-scarce regions using a machine learning algorithm. J Hydrol 598:126454","journal-title":"J Hydrol"},{"key":"11538_CR37","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2020.108037","volume":"291","author":"L Bigaignon","year":"2020","unstructured":"Bigaignon L, Fieuzal R, Delon C, Tallec T (2020) Combination of two methodologies, artificial neural network and linear interpolation, to gap-fill daily nitrous oxide flux measurements. Agric For Meteorol 291:108037","journal-title":"Agric For Meteorol"},{"issue":"10","key":"11538_CR38","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.3390\/atmos11101044","volume":"11","author":"O Bonacci","year":"2020","unstructured":"Bonacci O, Patekar M, Pola M, Roje-Bonacci T (2020) Analyses of climate variations at four meteorological domains on remote islands in the Croatian part of the Adriatic Sea. Atmosphere 11(10):1044","journal-title":"Atmosphere"},{"issue":"3\u20134","key":"11538_CR39","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/07055900.2022.2082369","volume":"60","author":"HB Bluestein","year":"2022","unstructured":"Bluestein HB, Carr FH, Goodman SJ (2022) Atmospheric observations of weather and climate. Atmos Ocean 60(3\u20134):149\u2013187","journal-title":"Atmos Ocean"},{"key":"11538_CR40","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106580","volume":"192","author":"JP Boomgard-Zagrodnik","year":"2022","unstructured":"Boomgard-Zagrodnik JP, Brown DJ (2022) Machine learning imputation of missing Mesonet temperature observations. Comput Electron Agric 192:106580","journal-title":"Comput Electron Agric"},{"key":"11538_CR41","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2023.116780","volume":"280","author":"EE Ba\u015fak\u0131n","year":"2023","unstructured":"Ba\u015fak\u0131n EE, Ekmekcio\u011flu \u00d6, \u00d6zger M (2023) Developing a novel approach for missing data imputation of solar radiation: A hybrid differential evolution algorithm based eXtreme gradient boosting model. Energy Convers Manage 280:116780","journal-title":"Energy Convers Manage"},{"issue":"4","key":"11538_CR42","doi-asserted-by":"publisher","DOI":"10.1002\/met.2018","volume":"28","author":"HC Bloomfield","year":"2021","unstructured":"Bloomfield HC, Brayshaw DJ, Gonzalez PLM, Charlton-Perez A (2021) Pattern-based conditioning enhances sub-seasonal prediction skill of European national energy variables. Meteorol Appl 28(4):e2018. https:\/\/doi.org\/10.1002\/met.2018","journal-title":"Meteorol Appl"},{"issue":"50","key":"11538_CR43","doi-asserted-by":"crossref","first-page":"75487","DOI":"10.1007\/s11356-022-21083-3","volume":"29","author":"H Citakoglu","year":"2022","unstructured":"Citakoglu H, Co\u015fkun \u00d6 (2022) Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Domain in Turkey. Environ Sci Pollut Res 29(50):75487\u201375511","journal-title":"Environ Sci Pollut Res"},{"issue":"1","key":"11538_CR44","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent neural networks for multivariate time series with missing values. Sci Rep 8(1):6085","journal-title":"Sci Rep"},{"key":"11538_CR45","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125126","volume":"588","author":"BD Chivers","year":"2020","unstructured":"Chivers BD, Wallbank J, Cole SJ, Sebek O, Stanley S, Fry M, Leontidis G (2020) Imputation of missing sub-hourly precipitation data in a large sensor network: a machine learning approach. J Hydrol 588:125126","journal-title":"J Hydrol"},{"key":"11538_CR46","volume":"316","author":"S Chen","year":"2022","unstructured":"Chen S, He C, Huang Z, Xu X, Jiang T, He Z, Liu J, Su B, Feng H, Yu Q, He J (2022) Using support vector machine to deal with the missing of solar radiation data in daily reference evapotranspiration estimation in China. Agric For Meteorol 316:108864","journal-title":"Agric For Meteorol"},{"key":"11538_CR47","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.apenergy.2018.05.054","volume":"225","author":"H Demirhan","year":"2018","unstructured":"Demirhan H, Renwick Z (2018) Missing value imputation for short to mid-term horizontal solar irradiance data. Appl Energy 225:998\u20131012","journal-title":"Appl Energy"},{"issue":"1","key":"11538_CR48","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1093\/jssam\/smab004","volume":"11","author":"M Dagdoug","year":"2023","unstructured":"Dagdoug M, Goga C, Haziza D (2023) Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison. J Surv Stat Methodol 11(1):141\u2013188","journal-title":"J Surv Stat Methodol"},{"key":"11538_CR49","doi-asserted-by":"crossref","unstructured":"Chakraborty D, Ba\u015fa\u011fao\u011flu H, Winterle J (2021) Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling. Expert Syst Appl 2021","DOI":"10.1016\/j.eswa.2020.114498"},{"issue":"7","key":"11538_CR50","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1002\/joc.1047","volume":"24","author":"S Feng","year":"2004","unstructured":"Feng S, Hu Q, Qian W (2004) Quality control of daily meteorological data in China, 1951\u20132000: a new dataset. Int J Climatol J R Meteorol Soc 24(7):853\u2013870","journal-title":"Int J Climatol J R Meteorol Soc"},{"issue":"10","key":"11538_CR51","doi-asserted-by":"crossref","first-page":"2832","DOI":"10.3390\/s20102832","volume":"20","author":"N Fouladgar","year":"2020","unstructured":"Fouladgar N, Fr\u00e4mling K (2020) A novel LSTM for multivariate time series with massive missingness. Sensors 20(10):2832","journal-title":"Sensors"},{"key":"11538_CR52","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1007\/s00024-019-02168-6","volume":"176","author":"I Gultepe","year":"2019","unstructured":"Gultepe I, Sharman R, Williams PD, Zhou B, Ellrod G, Minnis P, Gharabaghi B (2019) A review of high impact weather for aviation meteorology. Pure Appl Geophys 176:1869\u20131921","journal-title":"Pure Appl Geophys"},{"key":"11538_CR53","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s42044-020-00065-z","volume":"4","author":"I Gad","year":"2021","unstructured":"Gad I, Hosahalli D, Manjunatha B, Ghoneim OA (2021) A robust deep learning model for missing value imputation in big NCDC dataset. Iran J Comput Sci 4:67\u201384","journal-title":"Iran J Comput Sci"},{"issue":"6","key":"11538_CR54","doi-asserted-by":"crossref","first-page":"3393","DOI":"10.1016\/j.jksuci.2020.09.009","volume":"34","author":"K Jaseena","year":"2022","unstructured":"Jaseena K, Kovoor BC (2022) Deterministic weather forecasting models based on intelligent predictors: a survey. J King Saud Univ Comput Inf Sci 34(6):3393\u20133412","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"11538_CR55","doi-asserted-by":"crossref","unstructured":"Kaur S, Kokilev NN, Kuzie MR, Leung CK, Nguyen B, Pazdor AG, Shinnie MJ (2022) A big data science solution for transportation analytics with meteorological data. In: 2022 IEEE 16th international conference on big data science and engineering (BigDataSE)","DOI":"10.1109\/BigDataSE56411.2022.00013"},{"key":"11538_CR56","volume":"239","author":"W-C Lin","year":"2022","unstructured":"Lin W-C, Tsai C-F, Zhong JR (2022) Deep learning for missing value imputation of continuous data and the effect of data discretization. Knowl-Based Syst 239:108079","journal-title":"Knowl-Based Syst"},{"key":"11538_CR57","doi-asserted-by":"crossref","DOI":"10.1016\/j.pce.2020.102871","volume":"117","author":"N Mararakanye","year":"2020","unstructured":"Mararakanye N, Le Roux J, Franke A (2020) Using satellite-based weather data as input to SWAT in a data poor catchment. Phys Chem Earth Parts A\/B\/C 117:102871","journal-title":"Phys Chem Earth Parts A\/B\/C"},{"key":"11538_CR58","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.atmosres.2017.07.016","volume":"197","author":"JJ Mir\u00f3","year":"2017","unstructured":"Mir\u00f3 JJ, Caselles V, Estrela MJ (2017) Multiple imputation of rainfall missing data in the Iberian Mediterranean context. Atmos Res 197:313\u2013330","journal-title":"Atmos Res"},{"key":"11538_CR59","volume":"44","author":"J Ma","year":"2020","unstructured":"Ma J, Cheng JC, Ding Y, Lin C, Jiang F, Wang M, Zhai C (2020) Transfer learning for long-interval consecutive missing values imputation without external features in air pollution time series. Adv Eng Inform 44:101092","journal-title":"Adv Eng Inform"},{"key":"11538_CR60","doi-asserted-by":"publisher","first-page":"103667","DOI":"10.1016\/j.artint.2022.103667","volume":"306","author":"ML Baptista","year":"2022","unstructured":"Baptista ML, Goebel K, Henriques EMP (2022) Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artif Intell 306:103667. https:\/\/doi.org\/10.1016\/j.artint.2022.103667","journal-title":"Artif Intell"},{"key":"11538_CR61","doi-asserted-by":"crossref","DOI":"10.1016\/j.geomorph.2020.107303","volume":"367","author":"A Novak","year":"2020","unstructured":"Novak A, Popit T, Levani\u010d T, \u0160muc A, Kaczka RJ (2020) Debris flooding magnitude estimation based on relation between dendrogeomorphological and meteorological records. Geomorphology 367:107303","journal-title":"Geomorphology"},{"issue":"1","key":"11538_CR62","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1080\/1743727X.2014.979146","volume":"39","author":"M Pampaka","year":"2016","unstructured":"Pampaka M, Hutcheson G, Williams J (2016) Handling missing data: analysis of a challenging data set using multiple imputation. Int J Res Method Educ 39(1):19\u201337","journal-title":"Int J Res Method Educ"},{"key":"11538_CR63","doi-asserted-by":"crossref","unstructured":"Qiao Q, Yunusa-Kaltungo A, Edwards R (2020) Predicting building energy consumption based on meteorological data. 2020 IEEE PES\/IAS PowerAfrica","DOI":"10.1109\/PowerAfrica49420.2020.9219909"},{"key":"11538_CR64","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.agrformet.2012.03.015","volume":"161","author":"J Ramirez-Villegas","year":"2012","unstructured":"Ramirez-Villegas J, Challinor A (2012) Assessing relevant climate data for agricultural applications. Agric For Meteorol 161:26\u201345","journal-title":"Agric For Meteorol"},{"key":"11538_CR65","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.renene.2018.03.070","volume":"126","author":"F Rodr\u00edguez","year":"2018","unstructured":"Rodr\u00edguez F, Fleetwood A, Galarza A, Font\u00e1n L (2018) Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control. Renew Energy 126:855\u2013864","journal-title":"Renew Energy"},{"key":"11538_CR66","doi-asserted-by":"crossref","unstructured":"Ribani R, Marengoni M (2019) A survey of transfer learning for convolutional neural networks. In: 2019 32nd SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T)","DOI":"10.1109\/SIBGRAPI-T.2019.00010"},{"key":"11538_CR67","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.agrformet.2017.02.011","volume":"237","author":"J Rhee","year":"2017","unstructured":"Rhee J, Im J (2017) Meteorological drought forecasting for ungauged areas based on machine learning: using long-range climate forecast and remote sensing data. Agric For Meteorol 237:105\u2013122","journal-title":"Agric For Meteorol"},{"key":"11538_CR68","volume":"70","author":"KKR Samal","year":"2021","unstructured":"Samal KKR, Panda AK, Babu KS, Das SK (2021) An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach. Sustain Cities Soc 70:102923","journal-title":"Sustain Cities Soc"},{"key":"11538_CR69","doi-asserted-by":"crossref","unstructured":"Saad M, Chaudhary M, Karray F, Gaudet V (2020) Machine learning based approaches for imputation in time series data and their impact on forecasting. In: 2020 IEEE international conference on systems, man, and cybernetics (SMC)","DOI":"10.1109\/SMC42975.2020.9283191"},{"key":"11538_CR70","volume":"36","author":"KKR Samal","year":"2021","unstructured":"Samal KKR, Babu KS, Das SK (2021) Multi-directional temporal convolutional artificial neural network for PM2.5 forecasting with missing values: a deep learning approach. Urban Clim 36:100800","journal-title":"Urban Clim"},{"issue":"1","key":"11538_CR71","first-page":"1078","volume":"14","author":"MT Sattari","year":"2020","unstructured":"Sattari MT, Falsafian K, Irvem A, Qasem SN (2020) Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall. Eng Appl Comput Fluid Mech 14(1):1078\u20131094","journal-title":"Eng Appl Comput Fluid Mech"},{"key":"11538_CR72","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.compag.2017.09.033","volume":"142","author":"A Shtiliyanova","year":"2017","unstructured":"Shtiliyanova A, Bellocchi G, Borras D, Eza U, Martin R, Carr\u00e8re P (2017) Kriging-based approach to predict missing air temperature data. Comput Electron Agric 142:440\u2013449","journal-title":"Comput Electron Agric"},{"issue":"10","key":"11538_CR73","doi-asserted-by":"crossref","first-page":"1966","DOI":"10.3390\/pr10101966","volume":"10","author":"AQ Md","year":"2022","unstructured":"Md AQ, Keshav J, Haneef S, Sivaraman AK, Tee KF (2022) A review on data-driven quality prediction in production process with machine learning for industry 4.0. Processes 10(10):1966","journal-title":"Processes"},{"issue":"18","key":"11538_CR74","doi-asserted-by":"crossref","first-page":"5861","DOI":"10.3390\/en14185861","volume":"14","author":"S Benammar","year":"2021","unstructured":"Benammar S, Tee KF (2021) Criticality analysis and maintenance of solar tower power plants by integrating the artificial intelligence approach. Energies 14(18):5861","journal-title":"Energies"},{"key":"11538_CR75","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s00704-012-0723-x","volume":"112","author":"C Yozgatligil","year":"2013","unstructured":"Yozgatligil C, Aslan S, Iyigun C, Batmaz I (2013) Comparison of missing value imputation methods in time series: the case of Turkish meteorological data. Theor Appl Climatol 112:143\u2013167","journal-title":"Theor Appl Climatol"},{"key":"11538_CR76","doi-asserted-by":"crossref","unstructured":"Zarchi M, Shahgholi M (2022a) An expert condition monitoring system via fusion of signal processing for vibration of industrial rotating machinery with unseen operational conditions. J Vib Eng Technol","DOI":"10.1007\/s42417-022-00702-w"},{"key":"11538_CR77","doi-asserted-by":"crossref","unstructured":"Zarchi M, Shahgholi M (2022b) A novel information fusion approach using weighted neural networks for intelligent multi-class diagnostics of rotating machinery with unseen working conditions. J Vib Control","DOI":"10.1177\/10775463221138172"},{"issue":"4","key":"11538_CR78","doi-asserted-by":"publisher","first-page":"723","DOI":"10.3390\/diagnostics13040723","volume":"13","author":"JJ Sonia","year":"2023","unstructured":"Sonia JJ, Jayachandran P, Mohan S, Sivaraman AK, Tee KF (2023) Machine-learning-based diabetes mellitus risk prediction using multi-layer neural network no-prop algorithm. Diagnostics 13(4):723. https:\/\/doi.org\/10.3390\/diagnostics13040723","journal-title":"Diagnostics"},{"issue":"4","key":"11538_CR79","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.3390\/biomedicines11041167","volume":"11","author":"MG Veerabaku","year":"2023","unstructured":"Veerabaku MG, Nithiyanantham J, Urooj S, Sivaraman AK, Tee KF (2023) Intelligent Bi-LSTM with architecture optimization for heart disease prediction in WBAN through optimal channel selection and feature selection. Biomedicines 11(4):1167. https:\/\/doi.org\/10.3390\/biomedicines11041167","journal-title":"Biomedicines"},{"key":"11538_CR80","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1007\/s00158-018-2135-8","volume":"59","author":"M Zarchi","year":"2019","unstructured":"Zarchi M, Attaran B (2019) Improved design of an active landing gear for a passenger aircraft using multi-objective optimization technique. Struct Multidisc Optim 59:1813\u20131833. https:\/\/doi.org\/10.1007\/s00158-018-2135-8","journal-title":"Struct Multidisc Optim"},{"issue":"11","key":"11538_CR81","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1080\/0305215X.2017.1278757","volume":"49","author":"M Zarchi","year":"2017","unstructured":"Zarchi M, Attaran B (2017) Performance improvement of an active vibration absorber subsystem for an aircraft model using a bees algorithm based on multi-objective intelligent optimization. Eng Optim 49(11):1905\u20131921. https:\/\/doi.org\/10.1080\/0305215X.2017.1278757","journal-title":"Eng Optim"},{"key":"11538_CR82","doi-asserted-by":"publisher","first-page":"9051","DOI":"10.1007\/s11760-024-03529-y","volume":"18","author":"M Zarchi","year":"2024","unstructured":"Zarchi M, Shahgholi M, Tee KF (2024) An adaptable physics-informed fault diagnosis approach via hybrid signal processing and transferable feature learning for structural\/machinery health monitoring. Signal Image Video Process 18:9051\u20139066. https:\/\/doi.org\/10.1007\/s11760-024-03529-y","journal-title":"Signal Image Video Process"},{"key":"11538_CR83","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s40808-025-02421-z","volume":"11","author":"M Zarchi","year":"2025","unstructured":"Zarchi M, Nazari RA, Tee KF (2025) Explainable multi-attribute machine learning via a hierarchical nature-inspired system toward predicting geological hazards. Model Earth Syst Environ 11:259. https:\/\/doi.org\/10.1007\/s40808-025-02421-z","journal-title":"Model Earth Syst Environ"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11538-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11538-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11538-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T06:05:03Z","timestamp":1759125903000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11538-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,22]]},"references-count":83,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["11538"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11538-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,22]]},"assertion":[{"value":"28 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}