Adam Elhadi, Mutanga Onisimo, Abdel-Rahman Elfatih M., Ismail Riyad (2014): Estimating standing biomass in papyrus ( Cyperus papyrus L.) swamp: exploratory of in situ hyperspectral indices and random forest regression. International Journal of Remote Sensing, 35, 693-714. https://doi.org/10.1080/01431161.2013.870676 Breiman L. (2001): Random forests. Machine Learning, 45: 5–32.https://doi.org/10.1023/A:1010933404324Cutler D. Richard, Edwards Thomas C., Beard Karen H., Cutler Adele, Hess Kyle T., Gibson Jacob, Lawler Joshua J. (2007): RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY. Ecology, 88, 2783-2792. https://doi.org/10.1890/07-0539.1Dombi George W., Nandi Partha, Saxe Jonathan M., Ledgerwood Anna M., Lucas Charles E. (1995): Prediction of Rib Fracture Injury Outcome by an Artificial Neural Network. The Journal of Trauma: Injury, Infection, and Critical Care, 39, 915-921. https://doi.org/10.1097/00005373-199511000-00016Fan Hailiu, Xuan Jianbang, Zhang Kaixuan, Jiang Jianlan (2018): Anticancer component identification from the extract of Dysosma versipellis and Glycyrrhiza uralensis based on support vector regression and mean impact value. Analytical Methods, 10, 371-380. https://doi.org/10.1039/C7AY02465GGhasemi Jahan B., Tavakoli Hossein (2013): Application of random forest regression to spectral multivariate calibration. Analytical Methods, 5, 1863–1871. https://doi.org/10.1039/c3ay26338jGrömping Ulrike (2009): Variable Importance Assessment in Regression: Linear Regression versus Random Forest. The American Statistician, 63, 308-319. https://doi.org/10.1198/tast.2009.08199Gunathilaka R. P. Dayani, Smart James C. R., Fleming Christopher M. (2017): The impact of changing climate on perennial crops: the case of tea production in Sri Lanka. Climatic Change, 140, 577-592 https://doi.org/10.1007/s10584-016-1882-zGunathilaka Rajapaksha P. D., Smart James C. R., Fleming Christopher M., Hasan Syezlin (2018): The impact of climate change on labour demand in the plantation sector: the case of tea production in Sri Lanka. Australian Journal of Agricultural and Resource Economics, 62, 480-500. https://doi.org/10.1111/1467-8489.12262Guo Zhen-hai, Wu Jie, Lu Hai-yan, Wang Jian-zhou (2011): A case study on a hybrid wind speed forecasting method using BP neural network. Knowledge-Based Systems, 24, 1048-1056. https://doi.org/10.1016/j.knosys.2011.04.019 Hong N.B., Takahashi Y., Yabe M. (2016): Environmental efficiency and economic losses of Vietnamese tea production: implications for cost savings and environmental protection. Journal of the Faculty of Agriculture Kyushu University, 61: 383–390.Hong Nguyen Bich, Yabe Mitsuyasu (2017): Improvement in irrigation water use efficiency: a strategy for climate change adaptation and sustainable development of Vietnamese tea production. Environment, Development and Sustainability, 19, 1247-1263. https://doi.org/10.1007/s10668-016-9793-8Hutengs Christopher, Vohland Michael (2016): Downscaling land surface temperatures at regional scales with random forest regression. Remote Sensing of Environment, 178, 127-141. https://doi.org/10.1016/j.rse.2016.03.006Ishak A.B. (2016): Variable selection using support vector regression and random forests: A comparative study. Intelligent Data Analysis, 20, 83-104. https://doi.org/10.3233/IDA-150795 Kouchaki-Penchah H., Nabavi-Pelesaraei A., O’Dwyer J., Sharifi M. (2017): Environmental management of tea production using joint of life cycle assessment and data envelopment analysis approaches. Environmental Progress & Sustainable Energy, 36: 1116–1122.Luo Songrong, Cheng Junsheng, Wei Kexiang (2016): A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery. Shock and Vibration, 2016, 1-10. https://doi.org/10.1155/2016/5141564 Mendez G., Lohr S. (2011): Estimating residual variance in random forest regression. Computational Statistics & Data Analysis, 55: 2937–2950.Peng Yaohao, Albuquerque Pedro Henrique Melo, Camboim de Sá Jader Martins, Padula Ana Julia Akaishi, Montenegro Mariana Rosa (2018): The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications, 97, 177-192. https://doi.org/10.1016/j.eswa.2017.12.004Qiao Yuhui, Halberg Niels, Vaheesan Saminathan, Scott Steffanie (2016): Assessing the social and economic benefits of organic and fair trade tea production for small-scale farmers in Asia: a comparative case study of China and Sri Lanka. Renewable Agriculture and Food Systems, 31, 246-257. https://doi.org/10.1017/S1742170515000162Ren Chao, An Ning, Wang Jianzhou, Li Lian, Hu Bin, Shang Duo (2014): Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowledge-Based Systems, 56, 226-239. https://doi.org/10.1016/j.knosys.2013.11.015 UNFAO (2018): Database of Food and Agriculture Organization of the United Nations. UNFAO.Wang Huiyi, Kong Chunli, Li Dapeng, Qin Na, Fan Hongbing, Hong Hui, Luo Yongkang (2015): Modeling Quality Changes in Brined Bream (Megalobrama amblycephala) Fillets During Storage: Comparison of the Arrhenius Model, BP, and RBF Neural Network. Food and Bioprocess Technology, 8, 2429-2443. https://doi.org/10.1007/s11947-015-1595-8Wu Hongyan, Cai Yunpeng, Wu Yongsheng, Zhong Ren, Li Qi, Zheng Jing, Lin Denan, Li Ye (2017): Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression. BioScience Trends, 11, 292-296. https://doi.org/10.5582/bst.2017.01035Xiao Zhi, Huang Xianjin, Zang Zheng, Yang Hong (2018): Spatio-temporal variation and the driving forces of tea production in China over the last 30 years. Journal of Geographical Sciences, 28, 275-290. https://doi.org/10.1007/s11442-018-1472-2 Zhang Z., Jin X. (2018): Prediction of peak velocity of blasting vibration based on artificial neural network optimized by dimensionality reduction of FA-MIV. Mathematical Problems in Engineering, 2018: 1–12.Zhu Bangzhu, Han Dong, Wang Ping, Wu Zhanchi, Zhang Tao, Wei Yi-Ming (2017): Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression. Applied Energy, 191, 521-530. https://doi.org/10.1016/j.apenergy.2017.01.076