| [1] | Bergen J B, Johnson P A, de Hoop M V , et al. Machine learning for data-driven discovery in solid Earth geoscience[J]. Science, 2019,363(6433). DOI: 10.1126/science.aau0323 | | [2] | Kishtawal C M, Basu S, Patadia F , et al. Forecasting summer rainfall over India using genetic algorithm[J]. Geophysical Research Letters, 2003,30(23):2203 | | [3] | Lee Y, Wahba G, Ackerman S A . Cloud classification of satellite radiance data by multicategory support vector machines[J]. Journal of Atmospheric and Oceanic Technology, 2004,21(2):159-169 | | [4] | Coelho C A S, Pezzulli S, Balmaseda M , et al. Forecast calibration and combination: a simple Bayesian approach for ENSO[J]. Journal of Climate, 2004,17(7):1504-1516 | | [5] | Singh R, Kishtawal C M, Joshi P C . Estimation of monthly mean air-sea temperature difference from satellite observations using genetic algorithm[J]. Geophysical Research Letters, 2005,32(2):2232-2239 | | [6] | Tripathi S, Srinivas V V, Nanjundiah R S . Downscaling of precipitation for climate change scenarios: a support vector machine approach[J]. Journal of Hydrology, 2006,330(3-4):621-640 | | [7] | Luo L, Wood E F, Pan M . Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions[J]. Journal of Geophysical Research, 2007,112(D10):1-13 | | [8] | Deloncle A, Berk R, D’Andrea F , et al. Weather regime prediction using statistical learning[J]. Journal of the Atmospheric Sciences, 2007,64(5):1619-1635 | | [9] | Hong W C . Rainfall forecasting by technological machine learning models[J]. Applied Mathematics and Computation, 2008,200(1):41-57 | | [10] | Hrust L, Klaić Z B, Križan J , et al. Neural network forecasting of air pollutants hourly concentrations using optimized temporal averages of meteorological variables and pollutant concentrations[J]. Atmospheric Environment, 2009,43(35):5588-5596 | | [11] | Feng Y, Zhang W, Sun D , et al. Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification[J]. Atmospheric Environment, 2011,45(11):1979-1985 | | [12] | Mellit A, Pavan A M, Benghanem M . Least squares support vector machine for short-term prediction of meteorological time series[J]. Theoretical and Applied Climatology, 2013,111(1-2):297-307 | | [13] | Sun W, Zhang H, Palazoglu A , et al. Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California[J]. Science of the Total Environment, 2013,443:93-103 | | [14] | Ortizgarcia E G, Salcedosanz S, Casanovamateo C , et al. Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data[J]. Atmospheric Research, 2014: 128-136 | | [15] | Kumar R, Aggarwal R K, Sharma J D . Comparison of regression and artificial neural network models for estimation of global solar radiations[J]. Renewable and Sustainable Energy Reviews, 2015,52:1294-1299 | | [16] | Heinermann J, Kramer O . Machine learning ensembles for wind power prediction[J]. Renewable Energy, 2016,89:671-679 | | [17] | Ramseyer C A, Mote T L . Atmospheric controls on Puerto Rico precipitation using artificial neural networks[J]. Climate Dynamics, 2016,47(7-8):2515-2526 | | [18] | Zhang J, Liu P, Zhang F , et al. CloudNet: ground-based cloud classification with deep convolutional neural network[J]. Geophysical Research Letters, 2018,45(16):8665-8672 | | [19] | Jiang G Q, Xu J, Wei J . A deep learning algorithm of neural network for the parameterization of typhoon: ocean feedback in typhoon forecast models[J]. Geophysical Research Letters, 2018,45(8):3706-3716 | | [20] | Lin S Y, Chiang C C, Li J B , et al. Dynamic fine-tuning stacked auto-encoder neural network for weather forecast[J]. Future Generation Computer Systems, 2018,89:446-454 |
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