null | Aalto J, Karjalainen O, Hjort J, al et, 2018.Statistical forecasting of current and future circum-arctic ground temperatures and active layer thickness[J]. Geophysical Research Letters, 45(10): 4889-4898.DOI: 10.1029/2018gl078007 . |
null | Al-Anazi A F, Gates I D, 2012.Support vector regression to predict porosity and permeability: Effect of sample size[J]. Computers & Geosciences, 39: 64-76.DOI: 10.1016/j.cageo.2011.06.011 . |
null | Alizamir M, Kisi O, Ahmed A N, al et, 2020.Advanced machine learning model for better prediction accuracy of soil temperature at different depths[J]. PLoS One, 15(4): e0231055.DOI: 10. 1371/journal.pone.0231055 . |
null | Bilgili M, 2010.Prediction of soil temperature using regression and artificial neural network models[J]. Meteorology and Atmospheric Physics, 110(1/2): 59-70.DOI: 10.1007/s00703-010-0104-x . |
null | |
null | Brooks P D, McKnight D, Elder K, 2005.Carbon limitation of soil respiration under winter snowpacks: Potential feedbacks between growing season and winter carbon fluxes[J]. Global Change Biology, 11(2): 231-238.DOI: 10.1111/j.1365-2486.2004. 00877.x . |
null | Chen B F, Wang H D, Chu C C, 2007.Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea[J]. Ocean Engineering, 34(16): 2161-2175.DOI: 10.1016/j.oceaneng.2007.04.003 . |
null | Cherkassky V, Mulier F M, 2007.Learning from data: Concepts, theory, and methods[M].John Wiley & Sons. |
null | Citakoglu H, 2016.Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey[J]. Theoretical and Applied Climatology, 130(1/2): 545-556.DOI: 10.1007/s00704-016-1914-7 . |
null | Delbari M, Sharifazari S, Mohammadi E, 2018.Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques[J]. Theoretical and Applied Climatology, 135(3/4): 991-1001.DOI: 10.1007/s00704-018-2370-3 . |
null | Deluigi N, Lambiel C, 2013.PERMAL: A machine learning approach for alpine permafrost distribution modeling[M].Eidg.Forschungsanstalt WSL. |
null | Deluigi N, Lambiel C, Kanevski M, 2017.Data-driven mapping of the potential mountain permafrost distribution[J]. Science of The Total Environment, 590: 370-380.DOI: 10.1016/j.scitotenv. 2017.02.041 . |
null | Ding J, Tarokh V, Yang Y, 2018.Model selection techniques: An overview[J]. IEEE Signal Processing Magazine, 35(6): 16-34.DOI: 10.1109/MSP.2018.2867638 . |
null | Elith J, Leathwick J R, Hastie T, 2008.A working guide to boosted regression trees[J]. Journal of Animal Ecology, 77(4): 802-813.DOI: 10.1111/j.1365-2656.2008.01390.x . |
null | Fang X W, Luo S Q, Lyu S H, al et, 2021. Numerical modeling of the responses of soil temperature and soil moisture to climate change over the Tibetan Plateau, 1961-2010[J].International Journal of Climatology, 41(8): 4134-4150.DOI: 10.1002/joc.7062 . |
null | Feng Y, Cui N B, Hao W P, al et, 2019.Estimation of soil temperature from meteorological data using different machine learning models[J]. Geoderma, 338: 67-77.DOI: 10.1016/j.geoderma.2018.11.044 . |
null | Friedman J H, 2001.Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics, 1189-1232.DOI: 10.1214/aos/1013203451 . |
null | Gao S R, Wu Q B, Zhang Z Q, al et, 2020.Simulating active layer temperature based on weather factors on the Qinghai-Tibetan Plateau using ANN and wavelet-ANN models[J]. Cold Regions Science and Technology, 177.DOI: 10.1016/j.coldregions.2020. 103118 . |
null | García D H, 2021.Analysis and precision of the terrestrial surface temperature using Landsat 8 and Sentinel 3 images: Study applied to the city of Granada (Spain)[J]. Sustainable Cities and Society, 71.DOI: 10.1016/j.scs.2021.102980 . |
null | Gharabaghi B, Safadoust A, Mahboubi A A, al et, 2015.Temperature effect on the transport of bromide and E.coli NAR in saturated soils[J]. Journal of Hydrology, 522: 418-427.DOI: 10.1016/j.jhydrol.2015.01.003 . |
null | Gu J X, Wang Z H, Kuen J, al et, 2018.Recent advances in convolutional neural networks[J]. Pattern Recognition, 77: 354-377.DOI: 10.1016/j.patcog.2017.10.013 . |
null | Gunn S R, 1998.Support vector machines for classification and regression[J]. ISIS Technical Report, 14(1): 5-16.DOI: 10.1201/b10911-3 . |
null | Hao H B W, Yu F H, Li Q L, al et, 2020.Soil temperature prediction using convolutional neural network based on ensemble empirical mode decomposition[J]. IEEE Access, 9: 4084-4096.DOI: 10.1109/ACCESS.2020.3048028 . |
null | Hastie T J, 2017.Generalized additive models[M].Routledge. |
null | Heddam S, 2018.Development of air-soil temperature model using computational intelligence paradigms: Artificial neural network versus multiple linear regression[J]. Modeling Earth Systems and Environment, 5(3): 747-751.DOI: 10.1007/s40808-018-0565-3 . |
null | |
null | Hu J M, Wang J Z, Zeng G W, 2013.A hybrid forecasting approach applied to wind speed time series[J]. Renewable Energy, 60: 185-194.DOI: 10.1016/j.renene.2013.05.012 . |
null | Huang G B, Zhu Q Y, Siew C K, al et, 2004.Extreme learning machine: A new learning scheme of feedforward neural networks[C].2004 IEEE international joint conference on neural networks (IEEE Cat.No.04CH37541).IEEE, 2: 985-990.DOI: 10. 1109/ijcnn.2004.1380068 . |
null | Huang N E, Shen Z, Long S R, al et, 1998.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London.Series A: Mathematical, physical and engineering sciences, 454(1971): 903-995.DOI: 10.1098/rspa.1998.0193 . |
null | Huang R, Huang J X, Zhang C, al et, 2020.Soil temperature estimation at different depths, using remotely-sensed data[J]. Journal of Integrative Agriculture, 19(1): 277-290.DOI: 10.1016/s2095-3119(19)62657-2 . |
null | Hutengs C, Vohland M, 2016.Downscaling land surface temperatures at regional scales with random forest regression[J]. Remote Sensing of Environment, 178: 127-141.DOI: 10.1016/j.rse.2016. 03.006 . |
null | Jahanfar A, Drake J, Sleep B, al et, 2018.A modified FAO evapotranspiration model for refined water budget analysis for Green Roof systems[J]. Ecological Engineering, 119: 45-53.DOI: 10.1016/j.ecoleng.2018.04.021 . |
null | Jeong S, Park I, Kim H S, al et, 2021.Temperature prediction based on bidirectional long short-term memory and convolutional neural network combining observed and numerical forecast data[J]. Sensors (Basel), 21(3).DOI: 10.3390/s21030941 . |
null | Kang S, Kim S, Oh S, al et, 2000.Predicting spatial and temporal patterns of soil temperature based on topography, surface cover and air temperature[J]. Forest Ecology and Management, 136(1/3): 173-184.DOI: 10.1016/s0378-1127(99)00290-x . |
null | Kim S, Singh V P, 2014.Modeling daily soil temperature using data-driven models and spatial distribution[J]. Theoretical and Applied Climatology, 118(3): 465-479.DOI: 10.1007/s00704-013-1065-z . |
null | Kreuzer D, Munz M, Schlüter S, 2020.Short-term temperature forecasts using a convolutional neural network-An application to different weather stations in Germany[J]. Machine Learning with Applications, 2.DOI: 10.1016/j.mlwa.2020.100007 . |
null | Kumar A, Islam T, Sekimoto Y, al et, 2020.Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data[J]. PLoS One, 15(3): e0230114.DOI: 10.1371/journal.pone.0230114 . |
null | Lai L M, Zhao X C, Jiang L H, al et, 2012.Soil respiration in different agricultural and natural ecosystems in an arid region[J]. Plos One, 7(10): e48011.DOI: 10.1371/journal.pone.0048011 . |
null | LeCun Y, Bengio Y, 1995.Convolutional networks for images, speech, and time series[J].The Handbook of Brain Theory and Neural Networks, 3361(10): 1995 |
null | Li C, Zhang Y N, Ren X P, 2020a.Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network[J]. Algorithms, 13(7).DOI: 10.3390/a13070173 . |
null | Li Q L, Hao H B W, Zhao Y, al et, 2020b.GANs-LSTM model for soil temperature estimation from meteorological: A new approach[J]. IEEE Access, 8: 59427-59443.DOI: 10.1109/access. 2020.2982996 . |
null | Li Q L, Zhao Y, Yu F H, 2020c.A novel multichannel long short-term memory method with time series for soil temperature modeling[J]. IEEE Access, 8: 182026-182043.DOI: 10.1109/access.2020.3028995 . |
null | Liu J, Zhang T, Gou Y, al et, 2019.Convolutional LSTM networks for seawater temperature prediction[C].2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP).IEEE: 1- 53.DOI: 10.1109/icsidp47821.2019.9173301 . |
null | Luo S Q, Fang X W, Lyu S H, al et, 2016.Frozen ground temperature trends associated with climate change in the Tibetan Plateau Three River Source Region from 1980 to 2014[J]. Climate Research, 67(3): 241-255.DOI: 10.3354/cr01371 . |
null | Luo S Q, Fang X W, Lyu S H, al et, 2017.Improving CLM4.5 simulations of land-atmosphere exchange during freeze-thaw processes on the Tibetan Plateau[J]. Journal of Meteorological Research, 31(5): 916-930.DOI: 10.1007/s13351-017-6063-0 . |
null | Mehdizadeh S, Ahmadi F, Sales A K, 2020.Modelling daily soil temperature at different depths via the classical and hybrid models[J]. Meteorological Applications, 27(4).DOI: 10.1002/met. 1941 . |
null | |
null | Meikle R W, Treadway T R, 1981.A mathematical method for estimating soil temperatures in Canada[J]. Soil Science, 131(5): 320.DOI: 10.1097/00010694-198105000-00009 . |
null | Miao Q H, Pan B X, Wang H, al et, 2019.Improving monsoon precipitation prediction using combined convolutional and long short term memory neural network[J]. Water, 11(5).DOI: 10.3390/w11050977 . |
null | |
null | |
null | Mjolsness E, DeCoste D, 2001.Machine learning for science: state of the art and future prospects[J]. Science, 293(5537): 2051-2055.DOI: 10.1126/science.293.5537.2051 . |
null | Nahvi B, Habibi J, Mohammadi K, al et, 2016.Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature[J]. Computers and Electronics in Agriculture, 124: 150-160.DOI: 10.1016/j.compag.2016.03.025 . |
null | Napolitano G, Serinaldi F, See L, 2011.Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: An empirical examination[J]. Journal of Hydrology, 406(3-4): 199-214.DOI: 10.1016/j.jhydrol. 2011.06.015 . |
null | Nelder J A, Wedderburn R W M, 1972.Generalized linear models[J]. Journal of the Royal Statistical Society: Series A (General), 135(3): 370-384.DOI: 10.2307/2344614 . |
null | Ni J, Wu T H, Zhu X F, al et, 2021.Simulation of the present and future projection of permafrost on the Qinghai‐Tibet Plateau with statistical and machine learning models[J]. Journal of Geophysical Research: Atmospheres, 126(2).DOI: 10.1029/2020jd033402 . |
null | Nourani V, Alami M T, Aminfar M H, 2009.A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation[J]. Engineering Applications of Artificial Intelligence, 22(3): 466-472.DOI: 10.1016/j.engappai.2008.09.003 . |
null | Padarian J, Minasny B, McBratney A B, 2020.Machine learning and soil sciences: A review aided by machine learning tools[J]. Soil, 6(1): 35-52.DOI: 10.5194/soil-6-35-2020 . |
null | Paul K I, Polglase P J, Smethurst P J, al et, 2004.Soil temperature under forests: A simple model for predicting soil temperature under a range of forest types[J]. Agricultural and Forest Meteorology, 121(3-4): 167-182.DOI: 10.1016/j.agrformet.2003.08.030 . |
null | Ran Y H, Li X, Cheng G D, 2018.Climate warming over the past half century has led to thermal degradation of permafrost on the Qinghai-Tibet Plateau[J]. The Cryosphere, 12(2): 595-608.DOI: 10.5194/tc-12-595-2018 . |
null | Ran Y H, Li X, Cheng G D, al et, 2020.Mapping the permafrost stability on the Tibetan Plateau for 2005-2015[J]. Science China Earth Sciences, 64(1): 62-79.DOI: 10.1007/s11430-020-9685-3 . |
null | Reichstein M, Camps-Valls G, Stevens B, al et, 2019.Deep learning and process understanding for data-driven Earth system science[J]. Nature, 566(7743): 195-204.DOI: 10.1038/s41586-019-0912-1 . |
null | Samadianfard S, Asadi E, Jarhan S, al et, 2018a.Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths[J]. Soil and Tillage Research, 175: 37-50.DOI: 10.1016/j.still.2017.08.012 . |
null | Samadianfard S, Ghorbani M A, Mohammadi B, 2018b.Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm[J]. Information Processing in Agriculture, 5(4): 465-476.DOI: 10. 1016/j.inpa.2018.06.005 . |
null | Sandri M, Zuccolotto P, 2006.Variable selection using random forests[M]. Data analysis, classification and the forward search.Springer: 263-270.[C]//Zani S,Cerioli A,Riani M,et al(eds),Data analysis,classification and the forward search.Berlin,Heidelberg:Springer,263-270.DOI: 10.1007/3-540-35978-8_30. |
null | Sang Y F, Wang Z G, Liu C M, 2012.Period identification in hydrologic time series using empirical mode decomposition and maximum entropy spectral analysis[J]. Journal of Hydrology, 424-425: 154-164.DOI: 10.1016/j.jhydrol.2011.12.044 . |
null | |
null | Shi X J, Chen Z R, Wang H, al et, 2015.Convolutional LSTM network: A machine learning approach for precipitation nowcasting[C].Advances in neural information processing systems 28(NIPS 2015).Curran Associates,Inc,802-810. |
null | Shi X J, Gao Z H, Lausen L, al et, 2017.Deep learning for precipitation nowcasting: A benchmark and a new model[C].Advances in neural Information Processing Systems 30(NIPS 2017).Curran Associates,Inc. |
null | Shi Y Y, Niu F J, Yang C S, al et, 2018.Permafrost presence/absence mapping of the Qinghai-Tibet Plateau based on multi-source remote sensing data[J]. Remote Sensing, 10(2).DOI: 10.3390/rs10020309 . |
null | Tabari H, Sabziparvar A A, Ahmadi M, 2010.Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region[J]. Meteorology and Atmospheric Physics, 110(3-4): 135-142.DOI: 10.1007/s00703-010-0110-z . |
null | Tan J C, NourEldeen N, Mao K B, al et, 2019.Deep learning convolutional neural network for the retrieval of land surface temperature from AMSR2 data in China[J]. Sensors (Basel), 19(13):2987.DOI: 10.3390/s19132987 . |
null | |
null | Toy T J, Kuhaida Jr A J, Munson B E, 1978.The prediction of mean monthly soil temperature from mean monthly air temperature[J]. Soil Science, 126(3): 181-189.DOI: 10.1097/00010694-197809000-00008 . |
null | Vapnik V N, 1999.An overview of statistical learning theory[J]. IEEE transactions on neural networks, 10(5): 988-999.DOI: 10.1109/72.788640 . |
null | Wagle S, Uttamani S, Dsouza S, al et, 2020.Predicting surface air temperature using convolutional long short-term memory networks[C].Iccce 2019.570: 183-188.DOI: 10.1007/978-981-13-8715-9_23 . |
null | Wang S F, Zhou J, Lei T J, al et, 2020a.Estimating land surface temperature from satellite passive microwave observations with the traditional neural network, deep belief network, and convolutional neural network[J]. Remote Sensing, 12(17).DOI: 10.3390/rs12172691 . |
null | Wang S F, Zhou J, Zhang X D, al et, 2021.Estimating land surface temperature from AMSR-E and AMSR2 data with convolutional neural network[C].EGU General Assembly Conference Abstracts.EGU21-14126.DOI: 10.5194/egusphere-egu21-14126 . |
null | Wang T H, Yang D W, Fang B J, al et, 2019.Data-driven mapping of the spatial distribution and potential changes of frozen ground over the Tibetan Plateau[J]. Sci Total Environ, 649: 515-525.DOI: 10.1016/j.scitotenv.2018.08.369 . |
null | Wang T H, Yang D W, Yang Y T, al et, 2020b.Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau[J]. Science Advances, 6(19): eaaz3513.DOI: 10.1126/sciadv.aaz3513 . |
null | Wang W C, Chau K W, Qiu L, al et, 2015.Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition[J]. Environmental Research, 139: 46-54.DOI: 10.1016/j.envres.2015.02.002 . |
null | Wei S K, Yang H, Song J X, al et, 2013.A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows[J]. Hydrological Sciences Journal, 58(2): 374-389.DOI: 10.1080/02626667.2012.754102 . |
null | Wu W, Tang X P, Guo N J, al et, 2012.Spatiotemporal modeling of monthly soil temperature using artificial neural networks[J]. The Oretical and Applied Climatology, 113(3/4): 481-494.DOI: 10.1007/s00704-012-0807-7 . |
null | Wu Z H, Huang N E, 2009.Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 1(1): 1-41.DOI: 10.1142/S1793536909000047 . |
null | Xing L, Li L H, Gong J K, al et, 2018.Daily soil temperatures predictions for various climates in United States using data-driven model[J]. Energy, 160: 430-440.DOI: 10.1016/j.energy. 2018.07.004 . |
null | Xu L Y, Li Q, Yu J, al et, 2020.Spatio-temporal predictions of SST time series in China’s offshore waters using a regional convolution long short-term memory (RC-LSTM) network[J]. International Journal of Remote Sensing, 41(9): 3368-3389.DOI: 10.1080/01431161.2019.1701724 . |
null | Yang Y T, Dong J Y, Sun X, al et, 2018.A CFCC-LSTM model for sea surface temperature prediction[J]. IEEE Geoscience and Remote Sensing Letters, 15(2): 207-211.DOI: 10.1109/lgrs. 2017.2780843 . |
null | Yuan Q Q, Shen H F, Li T W, al et, 2020.Deep learning in environmental remote sensing: Achievements and challenges[J]. Remote Sensing of Environment, 241.DOI: 10.1016/j.rse.2020. 111716 . |
null | Zeynoddin M, Bonakdari H, Ebtehaj I, al et, 2019.A reliable linear stochastic daily soil temperature forecast model[J]. Soil and Tillage Research, 189: 73-87.DOI: 10.1016/j.still.2018.12.023 . |
null | Zhang F P, Dai H C, Tang D S, 2014.A conjunction method of wavelet transform-particle swarm optimization-support vector machine for streamflow forecasting[J]. Journal of Applied Mathematics, 2014: 1-10.DOI: 10.1155/2014/910196 . |
null | Zhang K, Geng X P, Yan X H, 2020.Prediction of 3-D ocean temperature by multilayer convolutional LSTM[J]. IEEE Geoscience and Remote Sensing Letters, 17(8): 1303-1307.DOI: 10.1109/lgrs.2019.2947170 . |
null | Zhang L P, Zhang L F, Du B, 2016.Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 4(2): 22-40.DOI: 10.1109/mgrs.2016.2540798 . |
null | Zhang X K, Zhang Q W, Zhang G, al et, 2018.A novel hybrid data-driven model for daily land surface temperature forecasting using long short-term memory neural network based on ensemble empirical mode decomposition[J]. International Journal of Environmental Research and Public Health, 15(5).DOI: 10.3390/ijerph15051032 . |
null | Zhang Z, Dong Y, 2020.Temperature forecasting via convolutional recurrent neural networks based on time-series data[J]. Complexity, 2020: 1-8.DOI: 10.1155/2020/3536572 . |
null | |
null | |
null | |
null | |
null | 韩博, 吕世华, 奥银焕, 2011.金塔绿洲土壤中蒸发/凝结过程的初步分析[J].高原气象, 30(6): 1462-1471 |
null | 李新, 程国栋, 2002.冻土-气候关系模型评述[J].冰川冻土, 24(3): 315-321. |
null | |
null | |
null | 孙菽芬, 2005.陆面过程的物理、 生化机理和参数化模型[M].北京: 气象出版社. |
null | 吴青柏, 沈永平, 施斌, 2003.青藏高原冻土及水热过程与寒区生态环境的关系[J].冰川冻土, 25(3): 250-255. |
null | |
null | 周志华, 2016.机器学习[M].北京: 清华大学出版社. |