References
- Cortes, C. and Vapnik, V. (1995) Support-vector networks. Machine Learning, 20(3), 273-279. https://doi.org/10.1007/BF00994018
- Ho, T. K. (1995) Random Decision Forests (PDF). Proceedings of the 3rd International Conference on Document Analysis and Recognition. Montreal. QC. 14 - 16 August 1995. 278-282.
- Lee, G. H., Jung, S. H. and Lee, D. E. (2018) Comparison of physics-based and data-driven models for streamflow simulation a the Mekong river. Journal of Korea Water Resources Association, 51(6), 503-514. https://doi.org/10.3741/JKWRA.2018.51.6.503
- Park, J., Kim, K. T., and Lee, W.H. (2020) Recent advances in information and communications technology (ICT) and sensor technology for monitoring water quality, Water, 12(2), 510. https://doi.org/10.3390/w12020510
- Petajajarvi. J., Mikhaylov. K., Roivainen. A., Hanninen. T., and Pettissalo, M. (2015) On the coverage a LPWANs: range evaluation and channel attenuation model for LoRa technology. In Proceedings of 2015 14th International Conference on ITS Telecommunication (ITST), 55-59.
- Rokach, L. and Maimon, O. (2000) Data mining with decision trees: theory and applications. World Scientific Pub Co Inc.
- Raza, U., Kulkarni, P., and Sooriyabandara, M., (2017) Low power wide area networks: An overview. IEEE Communications Surveys & Tutorials, 19, 855-873. https://doi.org/10.1109/COMST.2017.2652320
- Stuart J. R. and Peter N. (2010) Artificial Intelligence: A Modern Approach, Third Edition. Prentice Hall