JKSWE

1

Alain G., Bengio Y., 2016, Understanding intermediate layers using linear classifier probes,, arXiv preprint arXiv:1610.01644.

2

Altunkaynak A., 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,, Vol. 400, No. 3-4, pp. 353-363

3

Arhonditsis G. B., Brett M. T., 2004, Evaluation of the current state of mechanistic aquatic biogeochemical modeling,, Marine Ecology Progress Series,, Vol. 271, pp. 13-26

4

Arhonditsis G. B., Adams-VanHarn B. A., Nielsen L., Stow C. A., Reckhow K. H., 2006, Evaluation of the current state of mechanistic aquatic biogeochemical modeling: citation analysis and future perspectives,, Environmental science & technology,, Vol. 40, No. 21, pp. 6547-6554

5

Baker R. E., Peña J. M., Jayamohan J., Jérusalem A., 2018, Mechanistic models versus machine learning, a fight worth fighting for the biological community?, 20170660., Biology letters,, Vol. 14, No. 5

6

Bau D., Zhou B., Khosla A., Oliva A., Torralba A., 2017, Network dissection: Quantifying interpretability of deep visual representations,, Proceedings of the IEEE conference on computer vision and pattern recognition,, pp. 6541-6549

7

Castelvecchi D., 2016, Can we open the black box of AI?, Nature News,, Vol. 538, No. 7623, pp. 20

8

Cha Y., Cho K. H., Lee H., Kang T., Kim J. H., 2017, The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers,, Water research,, Vol. 124, pp. 11-19

9

Cha Y., Park S. S., Kim K., Byeon M., Stow C. A., 2014, Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model,, Water Resources Research,, Vol. 50, No. 3, pp. 2518-2532

10

Cha Y., Park S. S., Lee H. W., Stow C. A., 2016, A Bayesian hierarchical approach to model seasonal algal variability along an upstream to downstream river gradient,, Water Resources Research,, Vol. 52, No. 1, pp. 348-357

11

Choi S. Y., 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,, Vol. 21, pp. 96-108

12

Cloern J. E., Jassby A. D., 2010, Patterns and scales of phytoplankton variability in estuarine-coastal ecosystems,, Estuaries and coasts,, Vol. 33, No. 2, pp. 230-241

13

Dabkowski P., Gal Y., 2017, Real time image saliency for black box classifiers,, Advances in Neural Information Processing Systems,, pp. 6967-6976

14

Dormann C. F., Schymanski S. J., Cabral J., Chuine I., Graham C., Hartig F., Kearney M., Morin X., Römermann C., Schröder B., Singer A., 2012, Correlation and process in species distribution models: bridging a dichotomy,, Journal of Biogeography,, Vol. 39, No. 12, pp. 2119-2131

15

Hutchinson L., Steiert B., Soubret A., Wagg J., Phipps A., Peck R., Charoin J. E., Ribba B., 2019, Models and machines: how deep learning will take clinical pharmacology to the next level, CPT:, pharmacometrics & systems pharmacology,, Vol. 8, No. 3, pp. 131

16

Jeong S. U., 2012, The state of the art of lake water quality modeling and applications,, [Korean Literature], Magazine of the Korean Society of Agricultural Engineers,, Vol. 54, No. 1, pp. 56-69

17

Kim H. G., Hong S., Jeong K. S., Kim D. K., 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,, Vol. 398, pp. 67-76

18

Kim H. K., Cho I. H., Hwang E. A., Kim Y. J., 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,, Vol. 16, No. 15, pp. 2681

19

Kim S. E., Seo I. W., 2015, Artificial neural network ensemble modeling with conjunctive data clustering for water quality prediction in rivers,, Journal of Hydro-Environment Research,, Vol. 9, No. 3, pp. 325-339

20

Kim S. E., Seo I. W., Choi S. Y., 2017, Assessment of water quality variation of a monitoring network using exploratory factor analysis and empirical orthogonal function,, Environmental Modelling & Software,, Vol. 94, pp. 21-35

21

Kratzert F., Klotz D., Herrnegger M., Sampson A. K., Hochreiter S., Nearing G. S., 2019, Toward improved predictions in ungauged basins: Exploiting the power of machine learning,, Water Resources Research,, Vol. 55, No. 12, pp. 11344-11354

22

Kwon Y. S., Bae M. J., Hwang S. J., Kim S. H., Park Y. S., 2015, Predicting potential impacts of climate change on freshwater fish in Korea,, Ecological Informatics,, Vol. 29, pp. 156-165

23

LeCun Y., Bengio Y., Hinton G., 2015, Deep learning,, Nature,, Vol. 521, No. 7553, pp. 436-444

24

Lee B., Kullman S. W., Yost E., Meyer M. T., Worley‐Davis L., Williams C. M., 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,, Vol. 10, No. 4, pp. 511-521

25

Lundberg S. M., Lee S. I., 2017, A unified approach to interpreting model predictions,, In Advances in neural information processing systems,, pp. 4765-4774

26

Ma J., Cheng J. C., Lin C., Tan Y., Zhang J., 2019, Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques,, 116885., Atmospheric Environment,, pp. 214

27

Michielsen A., Kalantari Z., Lyon S. W., Liljegren E., 2016, Predicting and communicating flood risk of transport infrastructure based on watershed characteristics,, Journal of environmental management,, Vol. 182, pp. 505-518

28

Özesmi S. L., Tan C. O., Özesmi U., 2006, Methodological issues in building, training, and testing artificial neural networks in ecological applications,, Ecological Modelling,, Vol. 195, No. 1-2, pp. 83-93

29

Papernot N., McDaniel P., 2018, Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning,, arXiv preprint arXiv:1803.04765.

30

Park Y., Cho K. H., Park J., Cha S. M., 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,, Vol. 502, pp. 31-41

31

Park Y., Pyo J., Kwon Y. S., Cha Y., Lee H., Kang T., Cho K. H., 2017, Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea,, Water research,, Vol. 126, pp. 319-328

32

Peters D. P., Havstad K. M., Cushing J., Tweedie C., Fuentes O., Villanueva-Rosales N., 2014, Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology,, Ecosphere,, Vol. 5, No. 6, pp. 1-15

33

Pyo J., Duan H., Ligaray M., Kim M., Baek S., Kwon Y. S., Lee H., Kang T., Kim K., Cha Y., Cho K. H., 2020, An integrative remote sensing application of stacked autoencoder for atmospheric correction and cyanobacteria estimation using hyperspectral imagery,, Remote Sensing,, Vol. 12, No. 7, pp. 1073

34

Randolph K., Wilson J., Tedesco L., Li L., Pascual D. L., Soyeux E., 2008, Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments,, chlorophyll a and phycocyanin, Remote Sensing of Environment,, Vol. 112, No. 11, pp. 4009-4019

35

Ribeiro M. T., Singh S., 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,, pp. 1135-1144

36

Robson B. J., Arhonditsis G. B., Baird M. E., Brebion J., Edwards K. F., Geoffroy L., Hébert 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., Steven A., 2018, Towards evidence-based parameter values and priors for aquatic ecosystem modelling,, Environmental modelling & software,, Vol. 100, pp. 74-81

37

Rohani A., Taki M., 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,, Vol. 115, pp. 411-422

38

Schuwirth N., Borgwardt F., Domisch S., Friedrichs M., Kattwinkel M., Kneis D., Kuemmerlen M., Langhans S. D., Martinez-López J., Vermeiren P., 2019, How to make ecological models useful for environmental management,, 108784, Ecological Modelling,, pp. 411

39

Shen C., 2018, A transdisciplinary review of deep learning research and its relevance for water resources scientists,, Water Resources Research,, Vol. 54, No. 11, pp. 8558-8593

40

Shin J., Yoon S., Cha Y., 2017, Prediction of cyanobacteria blooms in the lower Han River (South Korea) using ensemble learning algorithms,, Desalination and Water Treatment,, Vol. 84, pp. 31-39

41

Stow C. A., Cha Y., 2013, Are chlorophyll a–total phosphorus correlations useful for inference and prediction?, Environmental science & technology,, Vol. 47, No. 8, pp. 3768-3773

42

Tian W., Liao Z., Wang X., 2019, Transfer learning for neural network model in chlorophyll-a dynamics prediction,, Environmental Science and Pollution Research,, Vol. 26, No. 29, pp. 29857-29871

43

Wenger S. J., Olden J. D., 2012, Assessing transferability of ecological models: an underappreciated aspect of statistical validation,, Methods in Ecology and Evolution,, Vol. 3, No. 2, pp. 260-267

44

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., Heinänen 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., Sequeira A. M., 2018, Outstanding challenges in the transferability of ecological models,, Trends in ecology & evolution,, Vol. 33, No. 10, pp. 790-802

45

Yim I., Shin J., Lee H., Park S., Nam G., Kang T., Cho K. H., Cha Y., 2020, Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data,, 105879., Ecological Indicators,, Vol. 110

46

Zhai B., 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,, Vol. 635, pp. 644-658

JKSWE The Journal of
the Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal ofthe Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal of
the Korean Society on Water Environment