Short-term Lake Erie algal bloom prediction by classification and regression models

Water Research - Tập 232 - Trang 119710 - 2023
Haiping Ai1, Kai Zhang1, Jiachun Sun1, Huichun Zhang1
1Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland OH 44106, United States

Tài liệu tham khảo

Arhonditsis, 2004, Evaluation of the current state of mechanistic aquatic biogeochemical modeling, Mar. Ecol. Prog. Ser., 271, 13, 10.3354/meps271013 Arnold, 1998, Large area hydrologic modeling and assessment part I: model development 1, J. Am. Water Resour. Assoc., 34, 73, 10.1111/j.1752-1688.1998.tb05961.x Assel, 2005, Classification of annual Great Lakes ice cycles: winters of 1973–2002, J. Clim., 18, 4895, 10.1175/JCLI3571.1 Bertani, 2016, Probabilistically assessing the role of nutrient loading in harmful algal bloom formation in western Lake Erie, J. Great Lakes Res., 42, 1184, 10.1016/j.jglr.2016.04.002 Bertani, 2017, Tracking cyanobacteria blooms: do different monitoring approaches tell the same story?, Sci. Total Environ., 575, 294, 10.1016/j.scitotenv.2016.10.023 Breiman, 1996, Bagging predictors, Int. J. Mach. Learn. Cybern., 24, 123, 10.1007/BF00058655 Bridgeman, 2013, A novel method for tracking western Lake Erie Microcystis blooms, 2002–2011, J. Great Lakes Res., 39, 83, 10.1016/j.jglr.2012.11.004 Chaffin, 2013, Nitrogen constrains the growth of late summer cyanobacterial blooms in Lake Erie, Adv. Microbiol., 3, 16, 10.4236/aim.2013.36A003 Chaffin, 2014, Summer phytoplankton nutrient limitation in Maumee Bay of Lake Erie during high-flow and low-flow years, J. Great Lakes Res., 40, 524, 10.1016/j.jglr.2014.04.009 Chaffin, 2020, Effectiveness of a fixed-depth sensor deployed from a buoy to estimate water-column cyanobacterial biomass depends on wind speed, J. Environ. Sci., 93, 23, 10.1016/j.jes.2020.03.003 Chen, T. and Guestrin, C. 2016 Xgboost: a scalable tree boosting system, pp. 785–794. Chen, 2020, A review of the artificial neural network models for water quality prediction, Appl. Sci., 10, 5776, 10.3390/app10175776 Commission, 1972 Del Giudice, 2018, Long-term phosphorus loading and springtime temperatures explain interannual variability of hypoxia in a large temperate lake, Environ. Sci. Technol., 52, 2046, 10.1021/acs.est.7b04730 DePinto, 1986, Impact of phosphorus availability on modelling phytoplankton dynamics, Hydrobiol. Bull., 20, 225, 10.1007/BF02291165 Elliott, 2010, The seasonal sensitivity of cyanobacteria and other phytoplankton to changes in flushing rate and water temperature, Glob. Chang. Biol., 16, 864, 10.1111/j.1365-2486.2009.01998.x Fang, 2019, A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent, Sci. Total Environ., 695, 10.1016/j.scitotenv.2019.133776 Franks, 2018, Recent advances in modelling of harmful algal blooms, Glob. Ecol. Oceanogr. Harmful Algal Blooms, 359, 10.1007/978-3-319-70069-4_19 Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 1189 Gers, 2000, Learning to forget: continual prediction with LSTM, Neural Comput., 12, 2451, 10.1162/089976600300015015 Hampel, 2019, Ammonium recycling supports toxic Planktothrix blooms in Sandusky Bay, Lake Erie: evidence from stable isotope and metatranscriptome data, Harmful Algae, 81, 42, 10.1016/j.hal.2018.11.011 Hastie, 2009 Ho, 2017, Phytoplankton blooms in Lake Erie impacted by both long-term and springtime phosphorus loading, J. Great Lakes Res., 43, 221, 10.1016/j.jglr.2017.04.001 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Hunter, 2008, The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: a case study using high spatial resolution time-series airborne remote sensing, Limnol. Oceanogr., 53, 2391, 10.4319/lo.2008.53.6.2391 Joosse, 2011, Context for re-evaluating agricultural source phosphorus loadings to the Great Lakes, Can. J. Soil Sci., 91, 317, 10.4141/cjss10005 Kalcic, 2016, Engaging stakeholders to define feasible and desirable agricultural conservation in western Lake Erie watersheds, Environ. Sci. Technol., 50, 8135, 10.1021/acs.est.6b01420 Kalcic, 2019, Climate change and nutrient loading in the western Lake Erie basin: warming can counteract a wetter future, Environ. Sci. Technol., 53, 7543, 10.1021/acs.est.9b01274 Kane, 2014, Re-eutrophication of Lake Erie: correlations between tributary nutrient loads and phytoplankton biomass, J. Great Lakes Res., 40, 496, 10.1016/j.jglr.2014.04.004 Kratzert, 2018, Rainfall–runoff modelling using long short-term memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005, 10.5194/hess-22-6005-2018 Li, 2021, Machine learning classification algorithms for predicting Karenia brevis blooms on the West Florida shelf, J. Mar. Sci. Eng., 9, 999, 10.3390/jmse9090999 Maier, 1996, The use of artificial neural networks for the prediction of water quality parameters, Water Resour. Res., 32, 1013, 10.1029/96WR03529 Manning, 2019, Extending the forecast model: predicting Western Lake Erie harmful algal blooms at multiple spatial scales, J. Great Lakes Res., 45, 587, 10.1016/j.jglr.2019.03.004 Matisoff, 2005, Lake Erie trophic status collaborative study, J. Great Lakes Res., 31, 1, 10.1016/S0380-1330(05)70300-2 McHugh, 2012, Interrater reliability: the kappa statistic, Biochem. Med. (Zagreb), 22, 276, 10.11613/BM.2012.031 Michalak, 2013, Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions, Proc. Natl. Acad. Sci., 110, 6448, 10.1073/pnas.1216006110 Millie, 2014, Using artificial intelligence for CyanoHAB niche modeling: discovery and visualization of Microcystis–environmental associations within western Lake Erie, Can. J. Fish. Aquat.Sci., 71, 1642, 10.1139/cjfas-2013-0654 Moore, 2009, Recent trends in paralytic shellfish toxins in Puget Sound, relationships to climate, and capacity for prediction of toxic events, Harmful Algae, 8, 463, 10.1016/j.hal.2008.10.003 Newell, 2019, Reduced forms of nitrogen are a driver of non-nitrogen-fixing harmful cyanobacterial blooms and toxicity in Lake Erie, Harmful Algae, 81, 86, 10.1016/j.hal.2018.11.003 2021 Obenour, 2014, Using a B ayesian hierarchical model to improve L ake E rie cyanobacteria bloom forecasts, Water Resour. Res., 50, 7847, 10.1002/2014WR015616 Paerl, 2016, Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients, Harmful Algae, 54, 213, 10.1016/j.hal.2015.09.009 Paerl, 2020, Mitigating eutrophication and toxic cyanobacterial blooms in large lakes: the evolution of a dual nutrient (N and P) reduction paradigm, Hydrobiologia, 847, 4359, 10.1007/s10750-019-04087-y Paerl, 2008, Blooms like it hot, Science, 320, 57, 10.1126/science.1155398 Pyo, 2020, Using convolutional neural network for predicting cyanobacteria concentrations in river water, Water Res., 186, 10.1016/j.watres.2020.116349 Richards, 2010, Unusually large loads in 2007 from the Maumee and Sandusky Rivers, tributaries to Lake Erie, J. Soil Water Conserv., 65, 450, 10.2489/jswc.65.6.450 Rousso, 2020, A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes, Water Res., 10.1016/j.watres.2020.115959 Rowe, 2016, Vertical distribution of buoyant Microcystis blooms in a Lagrangian particle tracking model for short-term forecasts in Lake Erie, J. Geophys. Res.: Oceans, 121, 5296, 10.1002/2016JC011720 Sayers, 2019, Satellite monitoring of harmful algal blooms in the Western Basin of Lake Erie: a 20-year time-series, J. Great Lakes Res., 45, 508, 10.1016/j.jglr.2019.01.005 Scavia, 2016, A multi-model approach to evaluating target phosphorus loads for Lake Erie, J. Great Lakes Res., 42, 1139, 10.1016/j.jglr.2016.09.007 Sellner, 1997, Physiology, ecology, and toxic properties of marine cyanobacteria blooms, Limnol. Oceanogr., 42, 1089, 10.4319/lo.1997.42.5_part_2.1089 Stumpf, 2016, Forecasting annual cyanobacterial bloom biomass to inform management decisions in Lake Erie, J. Great Lakes Res., 42, 1174, 10.1016/j.jglr.2016.08.006 Stumpf, 2012, Interannual variability of cyanobacterial blooms in Lake Erie, PLoS One, 7, e42444, 10.1371/journal.pone.0042444 Tao, 2017, A hybrid EOF algorithm to improve MODIS cyanobacteria phycocyanin data quality in a highly turbid lake: bloom and nonbloom condition, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 4430, 10.1109/JSTARS.2017.2723079 Taunk, 2019, 1255 Thomas, 2018, The predictability of a lake phytoplankton community, over time-scales of hours to years, Ecol Lett, 21, 619, 10.1111/ele.12927 2021 Valbi, 2019, A model predicting the PSP toxic dinoflagellate Alexandrium minutum occurrence in the coastal waters of the NW Adriatic Sea, Sci. Rep., 9, 1, 10.1038/s41598-019-40664-w Verhamme, 2016, Development of the Western Lake Erie Ecosystem Model (WLEEM): application to connect phosphorus loads to cyanobacteria biomass, J. Great Lakes Res., 42, 1193, 10.1016/j.jglr.2016.09.006 Verma, 2015, Climate change impacts on flow, sediment and nutrient export in a Great Lakes watershed using SWAT, CLEAN–Soil, Air, Water, 43, 1464, 10.1002/clen.201400724 Williams, 1995, Gaussian processes for regression, Adv. Neural Inf. Process. Syst., 8, 514 Wynne, 2013, Evolution of a cyanobacterial bloom forecast system in western Lake Erie: development and initial evaluation, J. Great Lakes Res., 39, 90, 10.1016/j.jglr.2012.10.003 Wynne, 2011, Estimating cyanobacterial bloom transport by coupling remotely sensed imagery and a hydrodynamic model, Ecol. Appl., 21, 2709, 10.1890/10-1454.1 Xia, 2020, River algal blooms are well predicted by antecedent environmental conditions, Water Res., 185, 10.1016/j.watres.2020.116221 Xu, 2021, Contributions of external nutrient loading and internal cycling to cyanobacterial bloom dynamics in Lake Taihu, China: implications for nutrient management, Limnol. Oceanogr., 66, 1492, 10.1002/lno.11700 Xu, 2020, Using long short-term memory networks for river flow prediction, Hydrol. Res., 51, 1358, 10.2166/nh.2020.026 Yuan, 2020, Optimizing climate model selection for hydrological modeling: a case study in the Maumee River basin using the SWAT, J. Hydrol. (Amst.), 588, 10.1016/j.jhydrol.2020.125064 Zhong, 2022, Machine learning-assisted QSAR models on contaminant reactivity toward four oxidants: combining small data sets and knowledge transfer, Environ. Sci. Technol., 56, 681, 10.1021/acs.est.1c04883