Forecasting daily extreme temperatures in Chinese representative cities using artificial intelligence models
Tài liệu tham khảo
Abbass, 2022, A review of the global climate change impacts, adaptation, and sustainable mitigation measures, Environ. Sci. Pollut. Res., 29, 42539, 10.1007/s11356-022-19718-6
Afzali, 2012, The potential of artificial neural network technique in daily and monthly ambient air temperature prediction, Int. J. Environ. Sustain Dev., 33
Altan Dombaycı, 2009, Daily means ambient temperature prediction using artificial neural network method: a case study of Turkey, Renew. Energy, 34, 1158, 10.1016/j.renene.2008.07.007
Ancona, 2019, Explaining deep neural networks with a polynomial time algorithm for shapley value approximation, 272
Bauer, 2020
Bauer, 2015, The quiet revolution of numerical weather prediction, Nature, 525, 47, 10.1038/nature14956
Betts, 2019, Near-surface biases in ERA5 over the Canadian prairies, Front. Environ. Sci., 7, 129, 10.3389/fenvs.2019.00129
Bi, 2022
Brunbjerg, 2018, Can patterns of urban biodiversity be predicted using simple measures of green infrastructure?, Urban For. Urban Green., 32, 143, 10.1016/j.ufug.2018.03.015
Chai, 2022, A calculation model for ground surface temperature in high-altitude regions of the Qinghai-Tibet Plateau, China, Rem. Sens., 14, 5219, 10.3390/rs14205219
Chen, 2014, How many metrics are required to identify the effects of the landscape pattern on land surface temperature?, Ecol. Indicat., 45, 424, 10.1016/j.ecolind.2014.05.002
Chen, 2022, Daily weather forecasting based on deep learning model: a case study of shenzhen city, China, Atmosphere, 13, 1208, 10.3390/atmos13081208
Chen, 2018
Chen, 2020, A model output deep learning method for grid temperature forecasts in Tianjin area, Appl. Sci., 10, 5808, 10.3390/app10175808
Chen, 2016, XGBoost: a scalable tree boosting system, 785
Cohen, 2007, Feature selection via coalitional game theory, Neural Comput., 19, 1939, 10.1162/neco.2007.19.7.1939
De Jeses, 2001, Backpropagation through time for a general class of recurrent network, 2638
2006
Driscoll, 1992, Continentality: a basic climatic parameter re-examined, Int. J. Climatol., 12, 185, 10.1002/joc.3370120207
Freedman, 2007
Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 10.1214/aos/1013203451
Gough, 2020, Impact of coastalization on day-to-day temperature variability along China's East coast, J. Coast Res., 36, 451, 10.2112/JCOASTRES-D-19-00167.1
Graves, 2009, A novel connectionist system for unconstrained handwriting recognition, IEEE Trans. Pattern Anal. Mach. Intell., 31, 855, 10.1109/TPAMI.2008.137
Guyon, 1991, Structural risk minimization for character recognition
Hamada, 2013, Impacts of land use and topography on the cooling effect of green areas on surrounding urban areas, Urban For. Urban Green., 12, 426, 10.1016/j.ufug.2013.06.008
Hassan, 2015, Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia, Environ. Earth Sci., 74, 463, 10.1007/s12665-015-4054-y
He, 2015
Hersbach, 2020, The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146, 1999, 10.1002/qj.3803
Hewage, 2021, Deep learning-based effective fine-grained weather forecasting model, Pattern Anal. Appl., 24, 343, 10.1007/s10044-020-00898-1
Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735
Huang, 2016, Spatial-temporal variation of aridity index of China during 1960–2013, Adv. Meteorol., 1
Izadi, 2021, Evaluation of ERA5 precipitation accuracy based on various time scales over Iran during 2000–2018, Water, 13, 2538, 10.3390/w13182538
Kang, 2011, Development of updateable model output statistics (UMOS) system for air temperature over South Korea, Asia-Pac. J. Atmospheric Sci., 47, 199, 10.1007/s13143-011-0009-8
Kingma, 2017
Koh, 2017, Understanding black-box predictions via influence functions, 1885
Krishnamupti, 2018
Lan, 2010, The effects of air temperature on office workers' well-being, workload and productivity-evaluated with subjective ratings, Appl. Ergon., 42, 29, 10.1016/j.apergo.2010.04.003
Lee, 2020, Forecasting daily temperatures with different time interval data using deep neural networks, Appl. Sci., 10, 1609, 10.3390/app10051609
Liang, 2022, Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network, Energy, 239, 10.1016/j.energy.2021.122210
Liu, 2014
Liu, 2014, Environmental effects of land-use/cover change caused by urbanization and policies in Southwest China Karst area – a case study of Guiyang, Habitat Int., 44, 339, 10.1016/j.habitatint.2014.07.009
Liu, 2022, Design optimization of the solar heating system for office buildings based on life cycle cost in Qinghai-Tibet plateau of China, Energy, 246, 10.1016/j.energy.2022.123288
Lundberg, 2017, A unified approach to interpreting model predictions
Molteni, 1996, The ECMWF ensemble prediction system: methodology and validation, Q. J. R. Meteorol. Soc., 122, 73, 10.1002/qj.49712252905
2020
Pal, 2003, Sofm-mlp: a hybrid neural network for atmospheric temperature prediction, IEEE Trans. Geosci. Rem. Sens., 41, 2783, 10.1109/TGRS.2003.817225
Pedregosa, 2018
Pinkus, 1999, Approximation theory of the MLP model in neural networks, Acta Numer., 8, 143, 10.1017/S0962492900002919
Preece, 1986, Multiple regression in hydrology, The Statistician, 35, 566, 10.2307/2987976
Rajendra, 2019, Use of ANN models in the prediction of meteorological data, Model. Earth Syst. Environ., 5, 1051, 10.1007/s40808-019-00590-2
Ramchoun, 2016
Ritchie, 1995, Implementation of the semi-Lagrangian method in a high-resolution version of the ECMWF forecast model, Mon. Weather Rev., 123, 489, 10.1175/1520-0493(1995)123<0489:IOTSLM>2.0.CO;2
Rozemberczki, 2022
Sardans, 2006, Warming and drought alter soil phosphatase activity and soil P availability in a Mediterranean shrubland, Plant Soil, 289, 227, 10.1007/s11104-006-9131-2
Schoof, 2001, Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks, Int. J. Climatol., 21, 773, 10.1002/joc.655
Schulte, 2016, Advancing the framework for considering the effects of climate change on worker safety and health, J. Occup. Environ. Hyg., 13, 847, 10.1080/15459624.2016.1179388
Sharchilev, 2018, Finding influential training samples for gradient boosted decision trees, 4577
Sharma, 2017, Activation functions in neural networks, Data Sci., 6, 310
Sharma, 2020, Activation functions in neural networks, Int. J. Eng. Appl. Sci. Technol., 310
Skamarock, 2005
Smola, 2004, A tutorial on support vector regression, Stat. Comput., 14, 199, 10.1023/B:STCO.0000035301.49549.88
Trenberth, 2001, Estimates of meridional atmosphere and ocean heat transports, J. Clim., 14, 3433, 10.1175/1520-0442(2001)014<3433:EOMAAO>2.0.CO;2
Ustaoglu, 2008, Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods, Meteorol. Appl., 15, 431, 10.1002/met.83
Veenhuis, 2013, Spread calibration of ensemble MOS forecasts, Mon. Weather Rev., 141, 2467, 10.1175/MWR-D-12-00191.1
Wenjia Kong, 2022, A deep spatio-temporal forecasting model for multi-site weather prediction post-processing, Commun. Comput. Phys., 31, 131, 10.4208/cicp.OA-2020-0158
Wilson, 2002, The Canadian updateable model output statistics (UMOS) system: design and development tests, Weather Forecast., 17, 206, 10.1175/1520-0434(2002)017<0206:TCUMOS>2.0.CO;2
Winter, 2002, The shapley value. Handb. Game Theory Econ, Appl, 3, 2025
Xu, 2009, A daily temperature dataset over China and its application in validating a RCM simulation, Adv. Atmos. Sci., 26, 763, 10.1007/s00376-009-9029-z
Yu, 2013, Detecting land use-water quality relationships from the viewpoint of ecological restoration in an urban area, Ecol. Eng., 53, 205, 10.1016/j.ecoleng.2012.12.045
Zhang, 2021, Qinghai-Tibet Plateau wetting reduces permafrost thermal responses to climate warming, Earth Planet Sci. Lett., 562, 10.1016/j.epsl.2021.116858
Zheng, 2013, The climate regionalization in China for 1981-2010, Chin. Sci. Bull., 58, 3088, 10.1360/972012-1491