{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T21:07:43Z","timestamp":1781212063784,"version":"3.54.1"},"reference-count":74,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.eswa.2026.133042","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:05:16Z","timestamp":1780013116000},"page":"133042","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Temperature prediction in mainland China: An integrated method based on multi-source data, temporal convolution, and channel attention"],"prefix":"10.1016","volume":"330","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8637-0407","authenticated-orcid":false,"given":"Peiyuan","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8239-2671","authenticated-orcid":false,"given":"Bowen","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"28","key":"10.1016\/j.eswa.2026.133042_b0005","doi-asserted-by":"crossref","first-page":"42539","DOI":"10.1007\/s11356-022-19718-6","article-title":"A review of the global climate change impacts, adaptation, and sustainable mitigation measures","volume":"29","author":"Abbass","year":"2022","journal-title":"Environmental Science and Pollution Research"},{"issue":"5","key":"10.1016\/j.eswa.2026.133042_b0010","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.3390\/math11051213","article-title":"Application of Advanced Optimized Soft Computing Models for Atmospheric Variable forecasting","volume":"11","author":"Adnan","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.eswa.2026.133042_b0015","unstructured":"Bai, S., Kolter, J. Z., & Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (Version 2). arXiv. https:\/\/doi.org\/10.48550\/ARX IV.1803.01271."},{"issue":"5","key":"10.1016\/j.eswa.2026.133042_b0020","doi-asserted-by":"crossref","first-page":"571","DOI":"10.5194\/npg-13-571-2006","article-title":"Nonlinear correlations of daily temperature records over land","volume":"13","author":"Bartos","year":"2006","journal-title":"Nonlinear Processes in Geophysics"},{"key":"10.1016\/j.eswa.2026.133042_b0025","article-title":"Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models","volume":"18(1), 10. https:\/\/doi.org\/10","author":"Bilgili","year":"2025","journal-title":"Earth Science Informatics"},{"key":"10.1016\/j.eswa.2026.133042_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.130516","article-title":"Enhancing rainfall prediction accuracy through image fusion of radar and numerical weather prediction models","volume":"303","author":"Byun","year":"2026","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0035","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1007\/s12145-023-01179-1","article-title":"A novel global average temperature prediction model\u2014\u2014Based on GM-ARIMA combination model","volume":"17","author":"Chen","year":"2024","journal-title":"Earth Science Informatics"},{"key":"10.1016\/j.eswa.2026.133042_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130128","article-title":"An approach of using social media data to detect the real time spatio-temporal variations of urban waterlogging","volume":"625","author":"Chen","year":"2023","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.eswa.2026.133042_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101819","article-title":"Long sequence time-series forecasting with deep learning: A survey","volume":"97","author":"Chen","year":"2023","journal-title":"Information Fusion"},{"issue":"16","key":"10.1016\/j.eswa.2026.133042_b0050","doi-asserted-by":"crossref","first-page":"4215","DOI":"10.3390\/en13164215","article-title":"Air Temperature forecasting using Machine Learning Techniques: A Review","volume":"13","author":"Cifuentes","year":"2020","journal-title":"Energies"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0055","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s12145-024-01530-0","article-title":"A hybrid physics-based method for estimating land surface temperature using radiative transfer simulations and machine learning model from Sentinel-3A SLSTR observations","volume":"18","author":"Dave","year":"2025","journal-title":"Earth Science Informatics"},{"key":"10.1016\/j.eswa.2026.133042_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.131081","article-title":"MCPT-CAF-BiGRU: A multi-scale CNN and ProbSparse-Masked Transformer model with cross-attention fusion and BiGRU for hourly wind speed forecasting","volume":"307","author":"Fan","year":"2026","journal-title":"Expert Systems with Applications"},{"issue":"11","key":"10.1016\/j.eswa.2026.133042_b0065","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.3390\/jmse10111686","article-title":"Predicting the Tropical Sea Surface Temperature Diurnal Cycle Amplitude using an improved XGBoost Algorithm","volume":"10","author":"Feng","year":"2022","journal-title":"Journal of Marine Science and Engineering"},{"key":"10.1016\/j.eswa.2026.133042_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.124179","article-title":"Load forecasting of district heating system based on Informer","volume":"253","author":"Gong","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.133042_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2025.146887","article-title":"A spatiotemporal deep learning approach to enhance air temperature estimation based on the sole input of land surface temperature","volume":"532","author":"Gong","year":"2025","journal-title":"Journal of Cleaner Production"},{"key":"10.1016\/j.eswa.2026.133042_b0080","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2020.02.016","article-title":"Analyzing spatial variability in night-time lights using a high spatial resolution color Jilin-1 image \u2013 Jerusalem as a case study","volume":"163","author":"Guk","year":"2020","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"issue":"9","key":"10.1016\/j.eswa.2026.133042_b0085","doi-asserted-by":"crossref","first-page":"22319","DOI":"10.1007\/s11356-022-23813-z","article-title":"Long-term projection of future climate change over the twenty-first century in the Sahara region in Africa under four Shared Socio-Economic Pathways scenarios","volume":"30","author":"Guo","year":"2022","journal-title":"Environmental Science and Pollution Research"},{"key":"10.1016\/j.eswa.2026.133042_b0090","doi-asserted-by":"crossref","DOI":"10.3389\/ffgc.2023.1249300","article-title":"Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models","volume":"6","author":"Guo","year":"2023","journal-title":"Frontiers in Forests and Global Change"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0095","doi-asserted-by":"crossref","first-page":"17748","DOI":"10.1038\/s41598-024-68906-6","article-title":"Monthly climate prediction using deep convolutional neural network and long short-term memory","volume":"14","author":"Guo","year":"2024","journal-title":"Scientific Reports"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0100","doi-asserted-by":"crossref","first-page":"6798","DOI":"10.1038\/s41598-025-91329-w","article-title":"Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City","volume":"15","author":"Guo","year":"2025","journal-title":"Scientific Reports"},{"issue":"12","key":"10.1016\/j.eswa.2026.133042_b0105","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.1007\/s00477-024-02840-x","article-title":"Multi-step prediction of greenhouse temperature and humidity based on temporal position attention LSTM","volume":"38","author":"Guo","year":"2024","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"issue":"4","key":"10.1016\/j.eswa.2026.133042_b0110","doi-asserted-by":"crossref","first-page":"254","DOI":"10.3390\/toxics13040254","article-title":"A Hybrid Wavelet-based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City","volume":"13","author":"He","year":"2025","journal-title":"Toxics"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0115","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3390\/toxics14010044","article-title":"Forecasting Daily Ambient PM2.5 Concentrations in Qingdao City using Deep Learning and Hybrid Interpretable Models and Analysis of Driving Factors using SHAP","volume":"14","author":"He","year":"2025","journal-title":"Toxics"},{"key":"10.1016\/j.eswa.2026.133042_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.pmedr.2025.103177","article-title":"Association between ambient temperature and injuries: A time series analysis using emergency ambulance dispatches in Shanghai","volume":"57","author":"Hu","year":"2025","journal-title":"Preventive Medicine Reports"},{"key":"10.1016\/j.eswa.2026.133042_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122466","article-title":"Thunderstorm prediction during pre-tactical air-traffic-flow management using convolutional neural networks","volume":"241","author":"Jardines","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"12","key":"10.1016\/j.eswa.2026.133042_b0130","doi-asserted-by":"crossref","first-page":"616","DOI":"10.3390\/ijgi11120616","article-title":"Spatial distribution of Urban Parks\u2019 effect on Air Pollution-Related Health and the Associated Factors in Beijing City","volume":"11","author":"Ji","year":"2022","journal-title":"ISPRS International Journal of Geo-Information"},{"key":"10.1016\/j.eswa.2026.133042_b0135","article-title":"Spatio-temporal variation of the relationship between air pollutants and land surface temperature in the Yangtze River Delta Urban Agglomeration","volume":"91","author":"Jiang","year":"2023","journal-title":"China. Sustainable Cities and Society"},{"issue":"4","key":"10.1016\/j.eswa.2026.133042_b0140","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1007\/s11600-020-00462-9","article-title":"Trend analysis and SARIMA forecasting of mean maximum and mean minimum monthly temperature for the state of Kerala","volume":"68","author":"Kabbilawsh","year":"2020","journal-title":"India. Acta Geophysica"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0145","doi-asserted-by":"crossref","first-page":"39027","DOI":"10.1038\/s41598-025-23455-4","article-title":"Multi-output deep learning for high-frequency prediction of air and surface temperature in Kuwait","volume":"15","author":"Khan","year":"2025","journal-title":"Scientific Reports"},{"key":"10.1016\/j.eswa.2026.133042_b0150","doi-asserted-by":"crossref","DOI":"10.3389\/fenvs.2025.1659344","article-title":"AI in extreme weather events prediction and response: A systematic topic-model review (2015\u20132024)","volume":"13","author":"Kim","year":"2025","journal-title":"Frontiers in Environmental Science"},{"issue":"7","key":"10.1016\/j.eswa.2026.133042_b0155","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1007\/s10462-025-11223-9","article-title":"A comprehensive survey of deep learning for time series forecasting: Architectural diversity and open challenges","volume":"58","author":"Kim","year":"2025","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.eswa.2026.133042_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.mlwa.2020.100007","article-title":"Short-term temperature forecasts using a convolutional neural network\u2014An application to different weather stations in Germany","volume":"2","author":"Kreuzer","year":"2020","journal-title":"Machine Learning with Applications"},{"issue":"S2","key":"10.1016\/j.eswa.2026.133042_b0165","doi-asserted-by":"crossref","first-page":"S739","DOI":"10.3103\/S0027134924702217","article-title":"An Overview of Machine Learning and Deep Learning applications in Earth Sciences in 2024: Achievements and Perspectives","volume":"79","author":"Krinitskiy","year":"2024","journal-title":"Moscow University Physics Bulletin"},{"issue":"4","key":"10.1016\/j.eswa.2026.133042_b0170","doi-asserted-by":"crossref","first-page":"3697","DOI":"10.1007\/s12145-023-01107-3","article-title":"Application of artificial neural network to screen out the dominant meteorological parameters for prediction of air temperature","volume":"16","author":"Kumar","year":"2023","journal-title":"Earth Science Informatics"},{"issue":"3","key":"10.1016\/j.eswa.2026.133042_b0175","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1175\/WAF-D-19-0158.1","article-title":"Use of the Autoregressive Integrated moving Average (ARIMA) Model to Forecast Near-Term Regional Temperature and Precipitation","volume":"35","author":"Lai","year":"2020","journal-title":"Weather and Forecasting"},{"key":"10.1016\/j.eswa.2026.133042_b0180","doi-asserted-by":"crossref","first-page":"28935","DOI":"10.1109\/ACCESS.2025.3539581","article-title":"Estimating Near-Surface Air Temperature from Satellite-Derived Land Surface Temperature using Temporal Deep Learning: A Comparative Analysis","volume":"13","author":"Lee","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.133042_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.eneco.2023.106575","article-title":"Warmer temperatures and energy poverty: Evidence from chinese households","volume":"120","author":"Li","year":"2023","journal-title":"Energy Economics"},{"issue":"2194","key":"10.1016\/j.eswa.2026.133042_b0190","article-title":"Time-series forecasting with deep learning: A survey","volume":"379","author":"Lim","year":"2021","journal-title":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences"},{"key":"10.1016\/j.eswa.2026.133042_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecotra.2020.100181","article-title":"How do subways affect urban passenger transport modes?\u2014Evidence from China","volume":"23","author":"Liu","year":"2020","journal-title":"Economics of Transportation"},{"issue":"16","key":"10.1016\/j.eswa.2026.133042_b0200","doi-asserted-by":"crossref","first-page":"5359","DOI":"10.1175\/JCLI-D-21-0447.1","article-title":"Correction of Overestimation in Observed Land Surface Temperatures based on Machine Learning Models","volume":"35","author":"Liu","year":"2022","journal-title":"Journal of Climate"},{"key":"10.1016\/j.eswa.2026.133042_b0205","doi-asserted-by":"crossref","unstructured":"Liu, L., & Wang, Q. (2022). Is the effect of human activity on air pollution linear or nonlinear? Evidence from Wuhan, China, under the COVID-19 lockdown. Cities, 127, 103752. https:\/\/doi.org\/10.1016 \/j.cities.2022.103752.","DOI":"10.1016\/j.cities.2022.103752"},{"key":"10.1016\/j.eswa.2026.133042_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129974","article-title":"A hybrid deep learning rainfall-runoff forecasting model Incorporating spatiotemporal information from multi-source data","volume":"298","author":"Liu","year":"2026","journal-title":"Expert Systems with Applications"},{"issue":"18","key":"10.1016\/j.eswa.2026.133042_b0215","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.3390\/rs17183209","article-title":"Deep Learning Retrieval and Prediction of Summer Average Near-Surface Air Temperature in China with Vegetation Regionalization","volume":"17","author":"Lu","year":"2025","journal-title":"Remote Sensing"},{"key":"10.1016\/j.eswa.2026.133042_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.130891","article-title":"An AI-driven data approach to enhancing climate resilience through climate change temperature prediction and early warning systems","volume":"305","author":"Majdoubi","year":"2026","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133042_b0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.jafrearsci.2025.105877","article-title":"Leveraging machine learning for accurate near-surface air temperature prediction to enhance climate adaptation in Ghana","volume":"233","author":"Oduro","year":"2026","journal-title":"Journal of African Earth Sciences"},{"issue":"10","key":"10.1016\/j.eswa.2026.133042_b0230","doi-asserted-by":"crossref","first-page":"6042","DOI":"10.3390\/su14106042","article-title":"Modeling the Impact of Weather and Context Data on Transport Mode choices: A Case Study of GPS Trajectories from Beijing","volume":"14","author":"Otim","year":"2022","journal-title":"Sustainability"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0235","doi-asserted-by":"crossref","first-page":"17208","DOI":"10.1038\/s41598-023-44286-1","article-title":"Predicting maximum temperatures over India 10-days ahead using machine learning models","volume":"13","author":"Ratnam","year":"2023","journal-title":"Scientific Reports"},{"key":"10.1016\/j.eswa.2026.133042_b0240","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2018.03.017","article-title":"NASA\u2019s Black Marble nighttime lights product suite","volume":"210","author":"Rom\u00e1n","year":"2018","journal-title":"Remote Sensing of Environment"},{"key":"10.1016\/j.eswa.2026.133042_b0245","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.procs.2020.11.005","article-title":"Forecasting the air temperature at a weather station using deep neural networks","volume":"178","author":"Roy","year":"2020","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.eswa.2026.133042_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.111692","article-title":"Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data","volume":"240","author":"Shen","year":"2020","journal-title":"Remote Sensing of Environment"},{"issue":"1\u20132","key":"10.1016\/j.eswa.2026.133042_b0255","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s00704-022-04173-7","article-title":"Weekend effect in summertime temperature and precipitation over the Yangtze River Delta region","volume":"150","author":"Song","year":"2022","journal-title":"Theoretical and Applied Climatology"},{"key":"10.1016\/j.eswa.2026.133042_b0260","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.tranpol.2024.10.027","article-title":"Beyond half-mile circle: Measuring the impact of subway expansion on home-based travels in Beijing, China","volume":"159","author":"Tan","year":"2024","journal-title":"Transport Policy"},{"issue":"3","key":"10.1016\/j.eswa.2026.133042_b0265","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1007\/s11600-023-01241-y","article-title":"Daily air temperature forecasting using LSTM-CNN and GRU-CNN models","volume":"72","author":"Uluocak","year":"2023","journal-title":"Acta Geophysica"},{"issue":"10","key":"10.1016\/j.eswa.2026.133042_b0270","doi-asserted-by":"crossref","first-page":"11107","DOI":"10.1007\/s13762-023-05092-4","article-title":"An efficient hybrid weather prediction model based on deep learning","volume":"20","author":"Utku","year":"2023","journal-title":"International Journal of Environmental Science and Technology"},{"key":"10.1016\/j.eswa.2026.133042_b0275","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, \u0141., & Polosukhin, I. (n.d.). Attention is All you Need."},{"issue":"15","key":"10.1016\/j.eswa.2026.133042_b0280","doi-asserted-by":"crossref","first-page":"4018","DOI":"10.3390\/su11154018","article-title":"An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature","volume":"11","author":"Wang","year":"2019","journal-title":"Sustainability"},{"issue":"8","key":"10.1016\/j.eswa.2026.133042_b0285","doi-asserted-by":"crossref","first-page":"3436","DOI":"10.3390\/su17083436","article-title":"Multi-Scale Temporal Integration for Enhanced Greenhouse Gas forecasting: Advancing climate Sustainability","volume":"17","author":"Wang","year":"2025","journal-title":"Sustainability"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0290","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/s41597-025-06505-4","article-title":"China Public Transport operation Network Dataset (CPTOND-2025):National-Scale Bus-Metro Vector Dataset","volume":"13","author":"Wang","year":"2026","journal-title":"Scientific Data"},{"key":"10.1016\/j.eswa.2026.133042_b0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127604","article-title":"AVI-Net: Audio-visual-integration inspired deep network with application to short-term air temperature forecasting","volume":"281","author":"Wu","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133042_b0300","first-page":"264","article-title":"Weather, travel mode choice, and impacts on subway ridership in Beijing","volume":"135","author":"Wu","year":"2020","journal-title":"Transportation Research Part A: Policy and Practice"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0305","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S2095-3119(20)63244-0","article-title":"Impact of climate change on maize yield in China from 1979 to 2016","volume":"20","author":"Wu","year":"2021","journal-title":"Journal of Integrative Agriculture"},{"key":"10.1016\/j.eswa.2026.133042_b0310","first-page":"1","article-title":"A Transformer Network Air Temperature and Humidity Inversion Method based on ATMS Brightness Temperature Data","volume":"22","author":"Xiao","year":"2025","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"10.1016\/j.eswa.2026.133042_b0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijdrr.2025.105865","article-title":"A novel MACD-based method of near real-time flood event detection from social media data","volume":"130","author":"Xiao","year":"2025","journal-title":"International Journal of Disaster Risk Reduction"},{"key":"10.1016\/j.eswa.2026.133042_b0320","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2025.106411","article-title":"Spatiotemporal impacts of purpose-specific human mobility on air pollution: Evidence from taxi trajectories and interpretable machine learning","volume":"126","author":"Xu","year":"2025","journal-title":"Sustainable Cities and Society"},{"issue":"3","key":"10.1016\/j.eswa.2026.133042_b0325","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s12145-025-01963-1","article-title":"A hybrid deep learning model in predicting weather temperature","volume":"18","author":"Yasavoli","year":"2025","journal-title":"Earth Science Informatics"},{"key":"10.1016\/j.eswa.2026.133042_b0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.eneco.2023.106973","article-title":"Temperature change and daily urban-rural residential electricity consumption in northwestern China: Responsiveness and inequality","volume":"126","author":"Zhang","year":"2023","journal-title":"Energy Economics"},{"issue":"1","key":"10.1016\/j.eswa.2026.133042_b0335","doi-asserted-by":"crossref","first-page":"82","DOI":"10.3390\/atmos16010082","article-title":"Machine Learning Methods for Weather forecasting: A Survey","volume":"16","author":"Zhang","year":"2025","journal-title":"Atmosphere"},{"key":"10.1016\/j.eswa.2026.133042_b0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2024.106043","article-title":"Relationship between land surface temperature and air quality in urban and suburban areas: Dynamic changes and interaction effects","volume":"118","author":"Zhang","year":"2025","journal-title":"Sustainable Cities and Society"},{"issue":"2","key":"10.1016\/j.eswa.2026.133042_b0345","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1007\/s12145-025-01895-w","article-title":"A deep learning approach for reconstructing hourly surface air temperature in Qinghai for the period 2006-2015","volume":"18","author":"Zhang","year":"2025","journal-title":"Earth Science Informatics"},{"key":"10.1016\/j.eswa.2026.133042_b0350","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2025.145449","article-title":"Three in Motion: A Mobile Study on the Interlinked Dynamics of CO2, Air Temperature, and PM2.5","volume":"506","author":"Zhang","year":"2025","journal-title":"Journal of Cleaner Production"},{"key":"10.1016\/j.eswa.2026.133042_b0355","doi-asserted-by":"crossref","DOI":"10.1016\/j.jag.2025.104703","article-title":"Spatiotemporal patterns of the urban thermal environment and the impact of human activities in low-latitude plateau cities","volume":"142","author":"Zhao","year":"2025","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"issue":"11","key":"10.1016\/j.eswa.2026.133042_b0360","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1109\/TITS.2017.2679179","article-title":"Spatio-temporal analysis of passenger travel patterns in massive smart card data","volume":"18","author":"Zhao","year":"2017","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.eswa.2026.133042_b0365","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 27268\u201327286). PMLR. https:\/\/proceedings.mlr.press\/v162\/zhou22 g.html."},{"key":"10.1016\/j.eswa.2026.133042_b0370","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.scitotenv.2019.02.077","article-title":"Reconstruction of high spatial resolution surface air temperature data across China: A new geo-intelligent multisource data-based machine learning technique","volume":"665","author":"Zhu","year":"2019","journal-title":"Science of The Total Environment"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426019536?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426019536?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T20:55:38Z","timestamp":1781211338000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426019536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":74,"alternative-id":["S0957417426019536"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133042","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Temperature prediction in mainland China: An integrated method based on multi-source data, temporal convolution, and channel attention","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133042","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133042"}}