{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:40:31Z","timestamp":1762353631964,"version":"3.44.0"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"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":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s12145-025-01728-w","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T02:31:13Z","timestamp":1738809073000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decompose-deep-recompose models and genetic algorithm based optimal ensemble method (GAE) to enhance the air temperature forecasting of world\u2019s major urban cities"],"prefix":"10.1007","volume":"18","author":[{"given":"Vipin","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"1728_CR1","doi-asserted-by":"publisher","first-page":"42539","DOI":"10.1007\/s11356-022-19718-6","volume":"29","author":"K Abbass","year":"2022","unstructured":"Abbass K, Qasim MZ, Song H et al (2022) A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environ Sci Pollut Res 29:42539\u201342559","journal-title":"Environ Sci Pollut Res"},{"key":"1728_CR2","doi-asserted-by":"crossref","unstructured":"Abu-Salih B, Wongthongtham P, Morrison G et\u00a0al (2022) Short-term renewable energy consumption and generation forecasting: a case study of western Australia. Heliyon 8","DOI":"10.1016\/j.heliyon.2022.e09152"},{"key":"1728_CR3","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1007\/s00704-019-02905-w","volume":"138","author":"P Aghelpour","year":"2019","unstructured":"Aghelpour P, Mohammadi B, Biazar SM (2019) Long-term monthly average temperature forecasting in some climate types of Iran, using the models Sarima, svr, and svr-fa. Theor Appl Climatol 138:1471\u20131480","journal-title":"Theor Appl Climatol"},{"key":"1728_CR4","doi-asserted-by":"publisher","first-page":"12","DOI":"10.17737\/tre.2020.6.1.00110","volume":"6","author":"A Ahmad","year":"2020","unstructured":"Ahmad A, Ghritlahre HK, Chandrakar P (2020) Implementation of ann technique for performance prediction of solar thermal systems: a comprehensive review. Trends Ren Energy 6:12\u201336","journal-title":"Trends Ren Energy"},{"key":"1728_CR5","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1007\/s00704-020-03459-y","volume":"143","author":"Y Akdi","year":"2021","unstructured":"Akdi Y, \u00dcnl\u00fc KD (2021) Periodicity in precipitation and temperature for monthly data of turkey. Theor Appl Climatol 143:957\u2013968","journal-title":"Theor Appl Climatol"},{"key":"1728_CR6","doi-asserted-by":"publisher","first-page":"e0277079","DOI":"10.1371\/journal.pone.0277079","volume":"17","author":"MK Alomar","year":"2022","unstructured":"Alomar MK, Khaleel F, Aljumaily MM et al (2022) Data-driven models for atmospheric air temperature forecasting at a continental climate region. Plos One 17:e0277079","journal-title":"Plos One"},{"key":"1728_CR7","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s12145-021-00583-9","volume":"14","author":"H Astsatryan","year":"2021","unstructured":"Astsatryan H, Grigoryan H, Poghosyan A et al (2021) Air temperature forecasting using artificial neural network for ararat valley. Earth Science Informatics 14:711\u2013722","journal-title":"Earth Science Informatics"},{"key":"1728_CR8","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.scitotenv.2019.04.347","volume":"681","author":"G Beig","year":"2019","unstructured":"Beig G, Srinivas R, Parkhi NS et al (2019) Anatomy of the winter 2017 air quality emergency in Delhi. Sci Total Environ 681:305\u2013311","journal-title":"Sci Total Environ"},{"key":"1728_CR9","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1080\/10962247.2017.1401017","volume":"68","author":"JE Bell","year":"2018","unstructured":"Bell JE, Brown CL, Conlon K et al (2018) Changes in extreme events and the potential impacts on human health. J Air Waste Manag Assoc 68:265\u2013287","journal-title":"J Air Waste Manag Assoc"},{"key":"1728_CR10","doi-asserted-by":"crossref","unstructured":"Bhatti UA, Yu Z, Chanussot J et al (2021) Local similarity-based spatial-spectral fusion hyperspectral image classification with deep cnn and gabor filtering. IEEE Trans Geosci Remote Sens 60:1\u201315","DOI":"10.1109\/TGRS.2021.3090410"},{"key":"1728_CR11","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1126\/science.add9065","volume":"377","author":"UA Bhatti","year":"2022","unstructured":"Bhatti UA, Nizamani MM, Mengxing H (2022) Climate change threatens Pakistan\u2019s snow leopards. Science 377:585\u2013586","journal-title":"Science"},{"key":"1728_CR12","doi-asserted-by":"crossref","unstructured":"Bhatti UA, Wu G, Bazai SU et\u00a0al (2022b) A pre-to post-covid-19 change of air quality patterns in anhui province using path analysis and regression. Pol J Environ Stud 31","DOI":"10.15244\/pjoes\/148065"},{"key":"1728_CR13","doi-asserted-by":"publisher","first-page":"132569","DOI":"10.1016\/j.chemosphere.2021.132569","volume":"288","author":"UA Bhatti","year":"2022","unstructured":"Bhatti UA, Zeeshan Z, Nizamani MM et al (2022) Assessing the change of ambient air quality patterns in Jiangsu province of china pre-to post-covid-19. Chemosphere 288:132569","journal-title":"Chemosphere"},{"key":"1728_CR14","doi-asserted-by":"publisher","first-page":"120496","DOI":"10.1016\/j.eswa.2023.120496","volume":"229","author":"UA Bhatti","year":"2023","unstructured":"Bhatti UA, Huang M, Neira-Molina H et al (2023) Mffcg-multi feature fusion for hyperspectral image classification using graph attention network. Expert Syst Appl 229:120496","journal-title":"Expert Syst Appl"},{"key":"1728_CR15","doi-asserted-by":"publisher","first-page":"137969","DOI":"10.1016\/j.jclepro.2023.137969","volume":"417","author":"UA Bhatti","year":"2023","unstructured":"Bhatti UA, Marjan S, Wahid A et al (2023) The effects of socioeconomic factors on particulate matter concentration in china\u2019s: new evidence from spatial econometric model. J Clean Prod 417:137969","journal-title":"J Clean Prod"},{"key":"1728_CR16","doi-asserted-by":"crossref","unstructured":"Chen P, Niu A, Liu D et\u00a0al (2018) Time series forecasting of temperatures using sarima: an example from nanjing. In: IOP Conference series: materials science and engineering. IOP Publishing, p 052024","DOI":"10.1088\/1757-899X\/394\/5\/052024"},{"key":"1728_CR17","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1007\/s12145-023-01179-1","volume":"17","author":"X Chen","year":"2024","unstructured":"Chen X, Jiang Z, Cheng H et al (2024) A novel global average temperature prediction model\u2013based on gm-arima combination model. Earth Sci Inform 17:853\u2013866","journal-title":"Earth Sci Inform"},{"key":"1728_CR18","doi-asserted-by":"publisher","first-page":"4215","DOI":"10.3390\/en13164215","volume":"13","author":"J Cifuentes","year":"2020","unstructured":"Cifuentes J, Marulanda G, Bello A et al (2020) Air temperature forecasting using machine learning techniques: a review. Energies 13:4215","journal-title":"Energies"},{"key":"1728_CR19","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41560-020-00771-9","volume":"6","author":"S Deutz","year":"2021","unstructured":"Deutz S, Bardow A (2021) Life-cycle assessment of an industrial direct air capture process based on temperature-vacuum swing adsorption. Nat Energy 6:203\u2013213","journal-title":"Nat Energy"},{"key":"1728_CR20","doi-asserted-by":"publisher","first-page":"123138","DOI":"10.1016\/j.apenergy.2024.123138","volume":"364","author":"J Ding","year":"2024","unstructured":"Ding J, Zhao P, Liu C et al (2024) From irregular to continuous: The deep koopman model for time series forecasting of energy equipment. Appl Energy 364:123138","journal-title":"Appl Energy"},{"key":"1728_CR21","doi-asserted-by":"publisher","first-page":"5407","DOI":"10.1007\/s11356-022-24240-w","volume":"30","author":"N El-Amarty","year":"2023","unstructured":"El-Amarty N, Marzouq M, El Fadili H et al (2023) A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends. Environ Sci Pollut Res 30:5407\u20135439","journal-title":"Environ Sci Pollut Res"},{"key":"1728_CR22","doi-asserted-by":"publisher","first-page":"122147","DOI":"10.1016\/j.eswa.2023.122147","volume":"238","author":"ESM El-Kenawy","year":"2024","unstructured":"El-Kenawy ESM, Khodadadi N, Mirjalili S et al (2024) Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst Appl 238:122147","journal-title":"Expert Syst Appl"},{"key":"1728_CR23","first-page":"17","volume":"1","author":"ESM El-Kenawy","year":"2024","unstructured":"El-Kenawy ESM, Rizk FH, Zaki AM et al (2024) Nioa: A novel metaheuristic algorithm modeled on the stealth and precision of Japanese ninjas. J Artif Intell Eng Pract 1:17\u201335","journal-title":"J Artif Intell Eng Pract"},{"key":"1728_CR24","doi-asserted-by":"publisher","first-page":"21","DOI":"10.54216\/JAIM.080103","volume":"8","author":"ESM El-Kenawy","year":"2024","unstructured":"El-Kenawy ESM, Rizk FH, Zaki AM et al (2024) Football optimization algorithm (fboa): a novel metaheuristic inspired by team strategy dynamics. J Artif Intell Metaheuristics 8:21\u201338","journal-title":"J Artif Intell Metaheuristics"},{"key":"1728_CR25","doi-asserted-by":"publisher","first-page":"757","DOI":"10.3390\/su15010757","volume":"15","author":"AM Elshewey","year":"2022","unstructured":"Elshewey AM, Shams MY, Elhady AM et al (2022) A novel wd-sarimax model for temperature forecasting using daily Delhi climate dataset. Sustainability 15:757","journal-title":"Sustainability"},{"key":"1728_CR26","doi-asserted-by":"publisher","first-page":"104151","DOI":"10.1016\/j.csite.2024.104151","volume":"55","author":"C Fan","year":"2024","unstructured":"Fan C, Zou B, Li J et al (2024) Exploring the relationship between air temperature and urban morphology factors using machine learning under local climate zones. Case Stud Therm Eng 55:104151","journal-title":"Case Stud Therm Eng"},{"key":"1728_CR27","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.foar.2021.12.005","volume":"11","author":"J Fu","year":"2022","unstructured":"Fu J, Dupre K, Tavares S et al (2022) Optimized greenery configuration to mitigate urban heat: a decade systematic review. Front Archit Res 11:466\u2013491","journal-title":"Front Archit Res"},{"key":"1728_CR28","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1007\/s00477-022-02358-0","volume":"37","author":"L Garc\u00eda-Duarte","year":"2023","unstructured":"Garc\u00eda-Duarte L, Cifuentes J, Marulanda G (2023) Short-term spatio-temporal forecasting of air temperatures using deep graph convolutional neural networks. Stoch Environ Res Risk Assess 37:1649\u20131667","journal-title":"Stoch Environ Res Risk Assess"},{"key":"1728_CR29","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s10661-023-11143-7","volume":"195","author":"K Gezici","year":"2023","unstructured":"Gezici K, \u015eeng\u00fcl S (2023) Estimation and analysis of missing temperature data in high altitude and snow-dominated regions using various machine learning methods. Environ Monit Assess 195:517","journal-title":"Environ Monit Assess"},{"key":"1728_CR30","doi-asserted-by":"publisher","first-page":"113476","DOI":"10.1016\/j.molliq.2020.113476","volume":"313","author":"M Ghalandari","year":"2020","unstructured":"Ghalandari M, Maleki A, Haghighi A et al (2020) Applications of nanofluids containing carbon nanotubes in solar energy systems: a review. J Mol Liq 313:113476","journal-title":"J Mol Liq"},{"key":"1728_CR31","doi-asserted-by":"crossref","unstructured":"Graf R, Aghelpour P (2021) Daily river water temperature prediction: A comparison between neural network and stochastic techniques. Atmosphere 12:1154","DOI":"10.3390\/atmos12091154"},{"key":"1728_CR32","doi-asserted-by":"publisher","first-page":"109623","DOI":"10.1016\/j.rser.2019.109623","volume":"121","author":"S Grafakos","year":"2020","unstructured":"Grafakos S, Viero G, Reckien D et al (2020) Integration of mitigation and adaptation in urban climate change action plans in Europe: a systematic assessment. Renew Sustain Energy Rev 121:109623","journal-title":"Renew Sustain Energy Rev"},{"key":"1728_CR33","doi-asserted-by":"publisher","first-page":"129566","DOI":"10.1016\/j.energy.2023.129566","volume":"286","author":"E Gulay","year":"2024","unstructured":"Gulay E, Sen M, Akgun OB (2024) Forecasting electricity production from various energy sources in t\u00fcrkiye: A predictive analysis of time series, deep learning, and hybrid models. Energy 286:129566","journal-title":"Energy"},{"key":"1728_CR34","unstructured":"Hausfather Z, for Atmospheric Research Staff\u00a0(Eds) NC (2022) The climate data guide: Global land-ocean surface temperature data: Hadcrut5. Retrieved from https:\/\/climatedataguide.ucar.edu\/climate-data\/global-land-ocean-surface-temperature-data-hadcrut5, last modified 2022-09-09. Accessed 09 Mar 2024"},{"key":"1728_CR35","doi-asserted-by":"publisher","first-page":"1962","DOI":"10.1080\/19475705.2022.2102942","volume":"13","author":"J Hou","year":"2022","unstructured":"Hou J, Wang Y, Zhou J et al (2022) Prediction of hourly air temperature based on cnn-lstm. Geomat Nat Haz Risk 13:1962\u20131986","journal-title":"Geomat Nat Haz Risk"},{"key":"1728_CR36","doi-asserted-by":"publisher","first-page":"2166235","DOI":"10.1080\/08839514.2023.2166235","volume":"37","author":"J Hou","year":"2023","unstructured":"Hou J, Wang Y, Hou B et al (2023) Spatial simulation and prediction of air temperature based on cnn-lstm. Appl Artif Intell 37:2166235","journal-title":"Appl Artif Intell"},{"key":"1728_CR37","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.3390\/su12052001","volume":"12","author":"CH Huang","year":"2020","unstructured":"Huang CH, Tsai HH, Chen HC (2020) Influence of weather factors on thermal comfort in subtropical urban environments. Sustainability 12:2001","journal-title":"Sustainability"},{"key":"1728_CR38","doi-asserted-by":"crossref","unstructured":"Janmaijaya M, Janmaijaya M, Muhuri PK (2024) A novel model based on spatio-temporal dilated convlstm networks for Indian ocean dipole forecasting using multi-source global sea surface temperature and heat content data. IEEE Access","DOI":"10.1109\/ACCESS.2024.3376520"},{"key":"1728_CR39","doi-asserted-by":"publisher","first-page":"e2023SW003763","DOI":"10.1029\/2023SW003763","volume":"22","author":"SH Jeong","year":"2024","unstructured":"Jeong SH, Lee WK, Kil H et al (2024) Deep learning-based regional ionospheric total electron content prediction\u2013long short-term memory (lstm) and convolutional lstm approach. Space Weather 22:e2023SW003763","journal-title":"Space Weather"},{"key":"1728_CR40","doi-asserted-by":"publisher","first-page":"11487","DOI":"10.1007\/s00521-020-05582-3","volume":"33","author":"C Johnstone","year":"2021","unstructured":"Johnstone C, Sulungu ED (2021) Application of neural network in prediction of temperature: a review. Neural Comput Appl 33:11487\u201311498","journal-title":"Neural Comput Appl"},{"key":"1728_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-021-09290-7","volume":"15","author":"O Katipoglu","year":"2022","unstructured":"Katipoglu O (2022) Prediction of missing temperature data using different machine learning methods. Arab J Geosci 15:1\u201311","journal-title":"Arab J Geosci"},{"key":"1728_CR42","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1007\/s00704-022-04103-7","volume":"149","author":"MI Khan","year":"2022","unstructured":"Khan MI, Maity R (2022) Hybrid deep learning approach for multi-step-ahead prediction for daily maximum temperature and heatwaves. Theor Appl Climatol 149:945\u2013963","journal-title":"Theor Appl Climatol"},{"key":"1728_CR43","doi-asserted-by":"publisher","first-page":"106262","DOI":"10.1016\/j.bspc.2024.106262","volume":"94","author":"S Khan","year":"2024","unstructured":"Khan S, Kumar V (2024) A novel hybrid gru-cnn and residual bias (rb) based rb-gru-cnn models for prediction of ptb diagnostic ecg time series data. Biomed Signal Process Control 94:106262","journal-title":"Biomed Signal Process Control"},{"key":"1728_CR44","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1038\/s41569-022-00720-x","volume":"19","author":"H Khraishah","year":"2022","unstructured":"Khraishah H, Alahmad B, Ostergard RL Jr et al (2022) Climate change and cardiovascular disease: implications for global health. Nat Rev Cardiol 19:798\u2013812","journal-title":"Nat Rev Cardiol"},{"key":"1728_CR45","doi-asserted-by":"crossref","unstructured":"Kudo K, Yamada M, Honda S, et\u00a0al (2021) Room-temperature two-terminal magnetoresistance ratio reaching 0.1% in semiconductor-based lateral devices with l21-ordered co2mnsi. Appl Phys Lett 118","DOI":"10.1063\/5.0045233"},{"key":"1728_CR46","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s11207-023-02209-3","volume":"298","author":"A Kumar","year":"2023","unstructured":"Kumar A, Kumar V (2023) Stacked 1d convolutional lstm (sconvlstm1d) model for effective prediction of sunspot time series. Sol Phys 298:121. https:\/\/doi.org\/10.1007\/s11207-023-02209-3","journal-title":"Sol Phys"},{"key":"1728_CR47","doi-asserted-by":"crossref","unstructured":"Kumar A, Kumar V (2024a) Forecast of solar cycle 25 based on hybrid cnn-bidirectional-gru (cnn-bigru) model and novel gradient residual correction (grc) technique. Advances in Space Research","DOI":"10.1016\/j.asr.2024.01.019"},{"key":"1728_CR48","doi-asserted-by":"publisher","first-page":"161","DOI":"10.5958\/2455-7145.2023.00022.X","volume":"22","author":"A Kumar","year":"2023","unstructured":"Kumar A, Sarangi A, Singh D et al (2023) Evaluation of an artificial intelligence algorithm for forecasting daily temperature. J Soil Water Conserv 22:161\u2013168","journal-title":"J Soil Water Conserv"},{"key":"1728_CR49","first-page":"1","volume":"12","author":"R Kumar","year":"2020","unstructured":"Kumar R, Liu X, Zhang J et al (2020) Room-temperature gas sensors under photoactivation: from metal oxides to 2d materials. Nanomicro Lett 12:1\u201337","journal-title":"Nanomicro Lett"},{"key":"1728_CR50","doi-asserted-by":"crossref","unstructured":"Kumar S, Kumar V (2024b) Multi-view stacked cnn-bilstm (mvs cnn-bilstm) for urban pm2. 5 concentration prediction of India\u2019s polluted cities. J Clean Prod 141259","DOI":"10.1016\/j.jclepro.2024.141259"},{"key":"1728_CR51","doi-asserted-by":"crossref","unstructured":"Kumar V, Kumar R (2024c) Incremental\u2013decremental data transformation based ensemble deep learning model (idt-edl) for temperature prediction. Model Earth Syst Environ 1\u201321","DOI":"10.1007\/s40808-024-01953-0"},{"key":"1728_CR52","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.3390\/app10051609","volume":"10","author":"S Lee","year":"2020","unstructured":"Lee S, Lee YS, Son Y (2020) Forecasting daily temperatures with different time interval data using deep neural networks. Appl Sci 10:1609","journal-title":"Appl Sci"},{"key":"1728_CR53","doi-asserted-by":"crossref","unstructured":"Li C, Zhang Y, Zhao G (2019) Deep learning with long short-term memory networks for air temperature predictions. In: 2019 International conference on artificial intelligence and advanced manufacturing (AIAM), IEEE, pp 243\u2013249","DOI":"10.1109\/AIAM48774.2019.00056"},{"key":"1728_CR54","doi-asserted-by":"publisher","first-page":"327","DOI":"10.7763\/JOCET.2014.V2.149","volume":"2","author":"N Liu","year":"2014","unstructured":"Liu N, Babushkin V, Afshari A (2014) Short-term forecasting of temperature driven electricity load using time series and neural network model. J Clean Energy Technol 2:327\u2013331","journal-title":"J Clean Energy Technol"},{"key":"1728_CR55","unstructured":"Liu Y, Racah E, Correa J et\u00a0al (2016) Application of deep convolutional neural networks for detecting extreme weather in climate datasets. arXiv:1605.01156"},{"key":"1728_CR56","doi-asserted-by":"publisher","first-page":"114078","DOI":"10.1016\/j.jenvman.2021.114078","volume":"303","author":"Z Liu","year":"2022","unstructured":"Liu Z, Lan J, Chien F et al (2022) Role of tourism development in environmental degradation: a step towards emission reduction. J Environ Manage 303:114078","journal-title":"J Environ Manage"},{"key":"1728_CR57","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.3390\/su13031318","volume":"13","author":"GS Malhi","year":"2021","unstructured":"Malhi GS, Kaur M, Kaushik P (2021) Impact of climate change on agriculture and its mitigation strategies: a review. Sustainability 13:1318","journal-title":"Sustainability"},{"key":"1728_CR58","unstructured":"Masson-Delmotte V, Zhai P, Portner H, et al (2018) Ipcc, 2018: summary for policymakers, global warming of 1.5$$^{\\circ }$$ c. An IPCC Special Report on the impacts of global warming of 1"},{"key":"1728_CR59","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s41324-023-00541-1","volume":"32","author":"A Mishra","year":"2024","unstructured":"Mishra A, Gupta Y (2024) Comparative analysis of air quality index prediction using deep learning algorithms. Spat Inf Res 32:63\u201372","journal-title":"Spat Inf Res"},{"key":"1728_CR60","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1007\/s00477-020-01898-7","volume":"35","author":"B Mohammadi","year":"2021","unstructured":"Mohammadi B, Mehdizadeh S, Ahmadi F et al (2021) Developing hybrid time series and artificial intelligence models for estimating air temperatures. Stoch Environ Res Risk Assess 35:1189\u20131204","journal-title":"Stoch Environ Res Risk Assess"},{"key":"1728_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3649448","volume":"56","author":"N Mohammadi Foumani","year":"2024","unstructured":"Mohammadi Foumani N, Miller L, Tan CW et al (2024) Deep learning for time series classification and extrinsic regression: a current survey. ACM Comput Surv 56:1\u201345","journal-title":"ACM Comput Surv"},{"key":"1728_CR62","doi-asserted-by":"publisher","first-page":"e2019JD032361","DOI":"10.1029\/2019JD032361","volume":"126","author":"CP Morice","year":"2021","unstructured":"Morice CP, Kennedy JJ, Rayner NA et al (2021) An updated assessment of near-surface temperature change from 1850: The hadcrut5 data set. J Geophys Res-Atmos 126:e2019JD032361","journal-title":"J Geophys Res-Atmos"},{"key":"1728_CR63","doi-asserted-by":"publisher","first-page":"108182","DOI":"10.1016\/j.petrol.2020.108182","volume":"200","author":"DA Otchere","year":"2021","unstructured":"Otchere DA, Ganat TOA, Gholami R et al (2021) Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: comparative analysis of ann and svm models. J Pet Sci Eng 200:108182","journal-title":"J Pet Sci Eng"},{"key":"1728_CR64","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s11600-018-0120-7","volume":"66","author":"G Papacharalampous","year":"2018","unstructured":"Papacharalampous G, Tyralis H, Koutsoyiannis D (2018) Predictability of monthly temperature and precipitation using automatic time series forecasting methods. Acta Geophys 66:807\u2013831","journal-title":"Acta Geophys"},{"key":"1728_CR65","doi-asserted-by":"publisher","first-page":"5207","DOI":"10.1007\/s11269-018-2155-6","volume":"32","author":"G Papacharalampous","year":"2018","unstructured":"Papacharalampous G, Tyralis H, Koutsoyiannis D (2018) Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: a multiple-case study from greece. Water Resour Manag 32:5207\u20135239","journal-title":"Water Resour Manag"},{"key":"1728_CR66","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1111\/irv.12682","volume":"14","author":"JE Park","year":"2020","unstructured":"Park JE, Son WS, Ryu Y et al (2020) Effects of temperature, humidity, and diurnal temperature range on influenza incidence in a temperate region. Influenza and Other Respiratory Viruses 14:11\u201318","journal-title":"Influenza and Other Respiratory Viruses"},{"key":"1728_CR67","doi-asserted-by":"publisher","first-page":"270","DOI":"10.3390\/rs16020270","volume":"16","author":"M Ra\u010di\u010d","year":"2024","unstructured":"Ra\u010di\u010d M, O\u0161tir K, Zupanc A et al (2024) Multi-year time series transfer learning: application of early crop classification. Remote Sens 16:270","journal-title":"Remote Sens"},{"key":"1728_CR68","doi-asserted-by":"crossref","unstructured":"Ravindra K, Bhardwaj S, Ram C et\u00a0al (2024) Temperature projections and heatwave attribution scenarios over India: a systematic review. Heliyon 10","DOI":"10.1016\/j.heliyon.2024.e26431"},{"key":"1728_CR69","doi-asserted-by":"crossref","unstructured":"Sabat NK, Pati UC, Das SK (2023) Abtcn: an efficient hybrid deep learning approach for atmospheric temperature prediction. Environ Sci Pollut Res 1\u201318","DOI":"10.1007\/s11356-023-27985-0"},{"key":"1728_CR70","first-page":"430","volume":"10","author":"Y Sari","year":"2022","unstructured":"Sari Y, Arifin YF, Novitasari N et al (2022) Deep learning approach using the gru-lstm hybrid model for air temperature prediction on daily basis. Int J Intell Syst Appl Eng 10:430\u2013436","journal-title":"Int J Intell Syst Appl Eng"},{"key":"1728_CR71","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1007\/s00703-021-00791-4","volume":"133","author":"A Sekertekin","year":"2021","unstructured":"Sekertekin A, Bilgili M, Arslan N et al (2021) Short-term air temperature prediction by adaptive neuro-fuzzy inference system (anfis) and long short-term memory (lstm) network. Meteorology and Atmospheric Physics 133:943\u2013959","journal-title":"Meteorology and Atmospheric Physics"},{"key":"1728_CR72","doi-asserted-by":"publisher","first-page":"186","DOI":"10.3390\/atmos10040186","volume":"10","author":"P Sekula","year":"2019","unstructured":"Sekula P, Bokwa A, Bochenek B et al (2019) Prediction of air temperature in the polish western carpathian mountains with the aladin-hirlam numerical weather prediction system. Atmosphere 10:186","journal-title":"Atmosphere"},{"key":"1728_CR73","doi-asserted-by":"publisher","first-page":"15601","DOI":"10.1007\/s00521-023-08580-3","volume":"35","author":"D Sen","year":"2023","unstructured":"Sen D, Huseyinoglu MF, G\u00fcnay ME (2023) Prediction of global temperature anomaly by machine learning based techniques. Neural Comput Appl 35:15601\u201315614","journal-title":"Neural Comput Appl"},{"key":"1728_CR74","doi-asserted-by":"publisher","first-page":"11068","DOI":"10.3390\/su151411068","volume":"15","author":"S Shen","year":"2023","unstructured":"Shen S, Du Y, Xu Z et al (2023) Temperature prediction based on stoa-svr rolling adaptive optimization model. Sustainability 15:11068","journal-title":"Sustainability"},{"key":"1728_CR75","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s12045-019-0924-z","volume":"25","author":"K Shivanna","year":"2020","unstructured":"Shivanna K (2020) The sixth mass extinction crisis and its impact on biodiversity and human welfare. Resonance 25:93\u2013109","journal-title":"Resonance"},{"key":"1728_CR76","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s43538-022-00073-6","volume":"88","author":"KR Shivanna","year":"2022","unstructured":"Shivanna KR (2022) Climate change and its impact on biodiversity and human welfare. Proc Indian Natl Sci Acad 88:160\u2013171","journal-title":"Proc Indian Natl Sci Acad"},{"key":"1728_CR77","doi-asserted-by":"crossref","unstructured":"Shrivastava VK, Shrivastava A, Sharma N et\u00a0al (2023) Deep learning model for temperature prediction: a case study in new Delhi. Journal of Forecasting","DOI":"10.1002\/for.2966"},{"key":"1728_CR78","doi-asserted-by":"publisher","first-page":"101390","DOI":"10.1016\/j.uclim.2022.101390","volume":"47","author":"J Siqi","year":"2023","unstructured":"Siqi J, Yuhong W, Ling C et al (2023) A novel approach to estimating urban land surface temperature by the combination of geographically weighted regression and deep neural network models. Urban Clim 47:101390","journal-title":"Urban Clim"},{"key":"1728_CR79","doi-asserted-by":"publisher","first-page":"626","DOI":"10.3390\/agronomy12030626","volume":"12","author":"M Soussi","year":"2022","unstructured":"Soussi M, Chaibi MT, Buchholz M et al (2022) Comprehensive review on climate control and cooling systems in greenhouses under hot and arid conditions. Agronomy 12:626","journal-title":"Agronomy"},{"key":"1728_CR80","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1126\/science.abf7136","volume":"374","author":"K Tang","year":"2021","unstructured":"Tang K, Dong K, Li J et al (2021) Temperature-adaptive radiative coating for all-season household thermal regulation. Science 374:1504\u20131509","journal-title":"Science"},{"key":"1728_CR81","unstructured":"Team G (2023) Giss surface temperature analysis (gistemp), version 4. NASA Goddard Institute for Space Studies. https:\/\/data.giss.nasa.gov\/gistemp"},{"key":"1728_CR82","doi-asserted-by":"crossref","unstructured":"Thi Kieu Tran T, Lee T, Shin JY et al (2020) Deep learning-based maximum temperature forecasting assisted with meta-learning for hyperparameter optimization. Atmosphere 11:487","DOI":"10.3390\/atmos11050487"},{"key":"1728_CR83","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.3390\/w13091294","volume":"13","author":"TTK Tran","year":"2021","unstructured":"Tran TTK, Bateni SM, Ki SJ et al (2021) A review of neural networks for air temperature forecasting. Water 13:1294","journal-title":"Water"},{"key":"1728_CR84","doi-asserted-by":"crossref","unstructured":"Uluocak I, Bilgili M (2023) Daily air temperature forecasting using lstm-cnn and gru-cnn models. Acta Geophys 1\u201320","DOI":"10.1007\/s11600-023-01241-y"},{"key":"1728_CR85","doi-asserted-by":"crossref","unstructured":"Waqas M, Naseem A, Humphries UW, et\u00a0al (2024) A comprehensive review of the impacts of climate change on agriculture in thailand. Farm Syst 100114","DOI":"10.1016\/j.farsys.2024.100114"},{"key":"1728_CR86","doi-asserted-by":"publisher","first-page":"1948","DOI":"10.3390\/atmos13121948","volume":"13","author":"S Wu","year":"2022","unstructured":"Wu S, Fu F, Wang L et al (2022) Short-term regional temperature prediction based on deep spatial and temporal networks. Atmosphere 13:1948","journal-title":"Atmosphere"},{"key":"1728_CR87","doi-asserted-by":"publisher","first-page":"101347","DOI":"10.1016\/j.uclim.2022.101347","volume":"47","author":"M Zhang","year":"2023","unstructured":"Zhang M, Kafy AA, Xiao P et al (2023) Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in wuhan, china. Urban Clim 47:101347","journal-title":"Urban Clim"},{"key":"1728_CR88","first-page":"1","volume":"2020","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Dong Y (2020) Temperature forecasting via convolutional recurrent neural networks based on time-series data. Complexity 2020:1\u20138","journal-title":"Complexity"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01728-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-025-01728-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01728-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:01:39Z","timestamp":1757138499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-025-01728-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,6]]},"references-count":88,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1728"],"URL":"https:\/\/doi.org\/10.1007\/s12145-025-01728-w","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"type":"print","value":"1865-0473"},{"type":"electronic","value":"1865-0481"}],"subject":[],"published":{"date-parts":[[2025,2,6]]},"assertion":[{"value":"2 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 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":"Not applicable. This study does not involve human participants or animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"237"}}