참고문헌
- Alain, G. and Bengio, Y. (2016). Understanding intermediate layers using linear classifier probes, arXiv preprint arXiv:1610.01644.
- Altunkaynak, A. and Wang, K. H. (2011). A comparative study of hydrodynamic model and expert system related models for prediction of total suspended solids concentrations in Apalachicola Bay, Journal of Hydrology, 400(3-4), 353-363. https://doi.org/10.1016/j.jhydrol.2011.01.046
- Arhonditsis, G. B. and Brett, M. T. (2004). Evaluation of the current state of mechanistic aquatic biogeochemical modeling, Marine Ecology Progress Series, 271, 13-26. https://doi.org/10.3354/meps271013
- Arhonditsis, G. B., Adams-VanHarn, B. A., Nielsen, L., Stow, C. A., and Reckhow, K. H. (2006). Evaluation of the current state of mechanistic aquatic biogeochemical modeling: citation analysis and future perspectives, Environmental science & technology, 40(21), 6547-6554. https://doi.org/10.1021/es061030q
- Baker, R. E., Pena, J. M., Jayamohan, J., and Jerusalem, A. (2018). Mechanistic models versus machine learning, a fight worth fighting for the biological community?, Biology letters, 14(5), 20170660. https://doi.org/10.1098/rsbl.2017.0660
- Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A. (2017). Network dissection: Quantifying interpretability of deep visual representations, Proceedings of the IEEE conference on computer vision and pattern recognition, 6541-6549.
- Castelvecchi, D. (2016). Can we open the black box of AI?, Nature News, 538(7623), 20. https://doi.org/10.1038/538020a
- Cha, Y., Cho, K. H., Lee, H., Kang, T., and Kim, J. H. (2017). The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers, Water research, 124, 11-19. https://doi.org/10.1016/j.watres.2017.07.040
- Cha, Y., Park, S. S., Kim, K., Byeon, M., and Stow, C. A. (2014). Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model, Water Resources Research, 50(3), 2518-2532. https://doi.org/10.1002/2013wr014372
- Cha, Y., Park, S. S., Lee, H. W., and Stow, C. A. (2016). A Bayesian hierarchical approach to model seasonal algal variability along an upstream to downstream river gradient, Water Resources Research, 52(1), 348-357. https://doi.org/10.1002/2015WR017327
- Choi, S. Y. and Seo, I. W. (2018). Prediction of fecal coliform using logistic regression and tree-based classification models in the North Han River, South Korea, Journal of Hydro-environment Research, 21, 96-108. https://doi.org/10.1016/j.jher.2018.09.002
- Cloern, J. E. and Jassby, A. D. (2010). Patterns and scales of phytoplankton variability in estuarine-coastal ecosystems, Estuaries and coasts, 33(2), 230-241. https://doi.org/10.1007/s12237-009-9195-3
- Dabkowski, P. and Gal, Y. (2017). Real time image saliency for black box classifiers, Advances in Neural Information Processing Systems, 6967-6976.
- Dormann, C. F., Schymanski, S. J., Cabral, J., Chuine, I., Graham, C., Hartig, F., Kearney, M., Morin, X., Romermann, C., Schroder, B., and Singer, A. (2012). Correlation and process in species distribution models: bridging a dichotomy, Journal of Biogeography, 39(12), 2119-2131. https://doi.org/10.1111/j.1365-2699.2011.02659.x
- Hutchinson, L., Steiert, B., Soubret, A., Wagg, J., Phipps, A., Peck, R., Charoin, J. E., and Ribba, B. (2019). Models and machines: how deep learning will take clinical pharmacology to the next level, CPT: pharmacometrics & systems pharmacology, 8(3), 131. https://doi.org/10.1002/psp4.12377
- Jeong, S. U. (2012). The state of the art of lake water quality modeling and applications, Magazine of the Korean Society of Agricultural Engineers, 54(1), 56-69. [Korean Literature]
- Kim, H. G., Hong, S., Jeong, K. S., Kim, D. K., and Joo, G. J. (2019). Determination of sensitive variables regardless of hydrological alteration in artificial neural network model of chlorophyll a: case study of Nakdong River, Ecological Modelling, 398, 67-76. https://doi.org/10.1016/j.ecolmodel.2019.02.003
- Kim, H. K., Cho, I. H., Hwang, E. A., Kim, Y. J., and Kim, B. H. (2019). Benthic diatom communities in Korean estuaries: Species appearances in relation to environmental variables, International journal of environmental research and public health, 16(15), 2681. https://doi.org/10.3390/ijerph16152681
- Kim, S. E. and Seo, I. W. (2015). Artificial neural network ensemble modeling with conjunctive data clustering for water quality prediction in rivers, Journal of Hydro-Environment Research, 9(3), 325-339. https://doi.org/10.1016/j.jher.2014.09.006
- Kim, S. E., Seo, I. W., and Choi, S. Y. (2017). Assessment of water quality variation of a monitoring network using exploratory factor analysis and empirical orthogonal function, Environmental Modelling & Software, 94, 21-35. https://doi.org/10.1016/j.envsoft.2017.03.035
- Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and Nearing, G. S. (2019). Toward improved predictions in ungauged basins: Exploiting the power of machine learning, Water Resources Research, 55(12), 11344-11354. https://doi.org/10.1029/2019wr026065
- Kwon, Y. S., Bae, M. J., Hwang, S. J., Kim, S. H., and Park, Y. S. (2015). Predicting potential impacts of climate change on freshwater fish in Korea, Ecological Informatics, 29, 156-165. https://doi.org/10.1016/j.ecoinf.2014.10.002
- LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning, Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
- Lee, B., Kullman, S. W., Yost, E., Meyer, M. T., Worley Davis, L., Williams, C. M., and Reckhow, K. H. (2014). A Bayesian network model for assessing natural estrogen fate and transport in a swine waste lagoon, Integrated environmental assessment and management, 10(4), 511-521. https://doi.org/10.1002/ieam.1538
- Lundberg, S. M. and Lee, S. I. (2017). A unified approach to interpreting model predictions, In Advances in neural information processing systems, 4765-4774.
- Ma, J., Cheng, J. C., Lin, C., Tan, Y., and Zhang, J. (2019). Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques, Atmospheric Environment, 214, 116885. https://doi.org/10.1016/j.atmosenv.2019.116885
- Michielsen, A., Kalantari, Z., Lyon, S. W., and Liljegren, E. (2016). Predicting and communicating flood risk of transport infrastructure based on watershed characteristics, Journal of environmental management, 182, 505-518. https://doi.org/10.1016/j.jenvman.2016.07.051
- Ozesmi, S. L., Tan, C. O., and Ozesmi, U. (2006). Methodological issues in building, training, and testing artificial neural networks in ecological applications, Ecological Modelling, 195(1-2), 83-93. https://doi.org/10.1016/j.ecolmodel.2005.11.012
- Papernot, N. and McDaniel, P. (2018). Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning, arXiv preprint arXiv:1803.04765.
- Park, Y., Cho, K. H., Park, J., Cha, S. M., and Kim, J. H. (2015). Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea, Science of the Total Environment, 502, 31-41. https://doi.org/10.1016/j.scitotenv.2014.09.005
- Park, Y., Pyo, J., Kwon, Y. S., Cha, Y., Lee, H., Kang, T., and Cho, K. H. (2017). Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea, Water research, 126, 319-328. https://doi.org/10.1016/j.watres.2017.09.026
- Peters, D. P., Havstad, K. M., Cushing, J., Tweedie, C., Fuentes, O., and Villanueva-Rosales, N. (2014). Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology, Ecosphere, 5(6), 1-15.
- Pyo, J., Duan, H., Ligaray, M., Kim, M., Baek, S., Kwon, Y. S., Lee, H., Kang, T., Kim, K., Cha, Y., and Cho, K. H. (2020). An integrative remote sensing application of stacked autoencoder for atmospheric correction and cyanobacteria estimation using hyperspectral imagery, Remote Sensing, 12(7), 1073. https://doi.org/10.3390/rs12071073
- Randolph, K., Wilson, J., Tedesco, L., Li, L., Pascual, D. L., and Soyeux, E. (2008). Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin, Remote Sensing of Environment, 112(11), 4009-4019. https://doi.org/10.1016/j.rse.2008.06.002
- Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier, Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135-1144.
- Robson, B. J., Arhonditsis, G. B., Baird, M. E., Brebion, J., Edwards, K. F., Geoffroy, L., Hebert, M. P., van Dongen-Vogels, V., Jones, E. M., Kruk, C., Mongin, M., Shimoda, Y., Skerratt, J. H., Trevathan-Tackett, S. M., Wild-Allen, K., Kong, X., and Steven, A. (2018). Towards evidence-based parameter values and priors for aquatic ecosystem modelling, Environmental modelling & software, 100, 74-81. https://doi.org/10.1016/j.envsoft.2017.11.018
- Rohani, A., Taki, M., and Abdollahpour, M. (2018). A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I), Renewable Energy, 115, 411-422. https://doi.org/10.1016/j.renene.2017.08.061
- Schuwirth, N., Borgwardt, F., Domisch, S., Friedrichs, M., Kattwinkel, M., Kneis, D., Kuemmerlen, M., Langhans, S. D., Martinez-Lopez, J., and Vermeiren, P. (2019). How to make ecological models useful for environmental management, Ecological Modelling, 411, 108784 https://doi.org/10.1016/j.ecolmodel.2019.108784
- Shen, C. (2018). A transdisciplinary review of deep learning research and its relevance for water resources scientists, Water Resources Research, 54(11), 8558-8593. https://doi.org/10.1029/2018wr022643
- Shin, J., Yoon, S., and Cha, Y. (2017). Prediction of cyanobacteria blooms in the lower Han River (South Korea) using ensemble learning algorithms, Desalination and Water Treatment, 84, 31-39. https://doi.org/10.5004/dwt.2017.20986
- Stow, C. A. and Cha, Y. (2013). Are chlorophyll a-total phosphorus correlations useful for inference and prediction?, Environmental science & technology, 47(8), 3768-3773. https://doi.org/10.1021/es304997p
- Tian, W., Liao, Z., and Wang, X. (2019). Transfer learning for neural network model in chlorophyll-a dynamics prediction, Environmental Science and Pollution Research, 26(29), 29857-29871. https://doi.org/10.1007/s11356-019-06156-0
- Wenger, S. J. and Olden, J. D. (2012). Assessing transferability of ecological models: an underappreciated aspect of statistical validation, Methods in Ecology and Evolution, 3(2), 260-267. https://doi.org/10.1111/j.2041-210X.2011.00170.x
- Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., Fielding, A. H., Bamford, A. J., Stephan, B., Barbosa, A. M., Dormann, C. F., Elith, J., Embling, C. B., Ervin, G. N., Fisher, R., Gould, S., Graf, R. F., Gregr, E. J., Halpin, P. N., Heikkinen, R. K., Heinanen, S., Mannocci, L., Mellin, C., Mesgaran, M. B., Moreno-Amat, E., Mormede, S., Novaczek, E., Oppel, S., Crespo, G. O., Peterson, A. T., Rapacciuolo, G., Roberts, J. J., Ross, R. E., Scales, K. L., Schoeman, D., Snelgrove, P., Sundblad, G., Thuiller, W., Torres, L. G., Verbruggen, H., Wang, L., Wenger, S., Whittingham, M. J., Zharikov, Y., Zurell, D., and Sequeira, A. M. (2018). Outstanding challenges in the transferability of ecological models, Trends in ecology & evolution, 33(10), 790-802. https://doi.org/10.1016/j.tree.2018.08.001
- Yim, I., Shin, J., Lee, H., Park, S., Nam, G., Kang, T., Cho, K. H., and Cha, Y. (2020). Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data, Ecological Indicators, 110, 105879. https://doi.org/10.1016/j.ecolind.2019.105879
- Zhai, B. and Chen, J. (2018). Development of a stacked ensemble model for forecasting and analyzing daily average PM2. 5 concentrations in Beijing, China, Science of The Total Environment, 635, 644-658. https://doi.org/10.1016/j.scitotenv.2018.04.040