Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes
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Shiqiang Wu | Jiangyu Dai | Senlin Zhu | Senlin Zhu | J. Dai | Shiqiang Wu | Wenguang Luo | Wenguang Luo
[1] Marijana Hadzima-Nyarko,et al. Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava , 2014, Water Resources Management.
[2] Joon Ha Kim,et al. Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea. , 2015, The Science of the total environment.
[3] Junfeng Gao,et al. An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in Lake Poyang, China , 2017, Ecol. Informatics.
[4] Young-Seuk Park,et al. EVALUATION OF ENVIRONMENTAL FACTORS ON CYANOBACTERIAL BLOOM IN EUTROPHIC RESERVOIR USING ARTIFICIAL NEURAL NETWORKS 1 , 2011, Journal of phycology.
[5] Weichun Ma,et al. Developing a PCA–ANN Model for Predicting Chlorophyll a Concentration from Field Hyperspectral Measurements in Dianshan Lake, China , 2015, Water Quality, Exposure and Health.
[6] Salim Heddam,et al. Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models , 2018, Environmental Science and Pollution Research.
[7] Seo Jin Ki,et al. Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: a case study of the Yeongsan Reservoir, Korea. , 2009, The Science of the total environment.
[8] Björn C. Rall,et al. Interactive effects of warming, eutrophication and size structure: impacts on biodiversity and food‐web structure , 2016, Global change biology.
[9] M. Hosomi,et al. Novel application of a back-propagation artificial neural network model formulated to predict algal bloom , 1997 .
[10] Richard Wood,et al. Trade and the role of non-food commodities for global eutrophication , 2018, Nature Sustainability.
[11] Ellen I. Damschen,et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands , 2014, Nature.
[12] F. Recknagel,et al. Artificial neural network approach for modelling and prediction of algal blooms , 1997 .
[13] Wei Li,et al. Forecasting short‐term cyanobacterial blooms in Lake Taihu, China, using a coupled hydrodynamic–algal biomass model , 2014 .
[14] Handan Çamdevýren,et al. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs , 2005 .
[15] David A. Culver,et al. Re-eutrophication of Lake Erie: Correlations between tributary nutrient loads and phytoplankton biomass , 2014 .
[16] Huaicheng Guo,et al. Exploring the influence of lake water chemistry on chlorophyll a: a multivariate statistical model analysis. , 2010 .
[17] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[18] D. Canfield,et al. Factors related to Secchi depths and their stability over time as determined from a probability sample of US lakes , 2017, Environmental Monitoring and Assessment.
[19] Hakan Yasarer,et al. Artificial Neural Network for Prediction of Total Nitrogen and Phosphorus in US Lakes , 2019, Journal of Environmental Engineering.
[20] Salim Heddam,et al. Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study , 2013, Environmental Monitoring and Assessment.
[21] S. Soyupak,et al. Case studies on the use of neural networks in eutrophication modeling , 2000 .
[22] David P. Hamilton,et al. Predicting the effects of climate change on trophic status of three morphologically varying lakes: Implications for lake restoration and management , 2011, Environ. Model. Softw..
[23] Ahmed El-Shafie,et al. Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring , 2014, Environmental Science and Pollution Research.
[24] Dong-Kyun Kim,et al. River phytoplankton prediction model by Artificial Neural Network: Model performance and selection of input variables to predict time-series phytoplankton proliferations in a regulated river system , 2006, Ecological Informatics.
[25] Peter A. Whigham,et al. Predicting chlorophyll-a in freshwater lakes by hybridising process-based models and genetic algorithms , 2001 .
[26] Seok Soon Park,et al. Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network , 2014 .
[27] Z. Li,et al. Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China , 2015, PloS one.
[28] Nicola Fohrer,et al. Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches , 2013, Limnology.
[29] Iyan E. Mulia,et al. Hybrid ANN–GA model for predicting turbidity and chlorophyll-a concentrations , 2013 .
[30] Zhi Chen,et al. Modeling chlorophyll-a concentrations using an artificial neural network for precisely eco-restoring lake basin , 2016 .
[31] Kwang-Seuk Jeong,et al. Determination of sensitive variables regardless of hydrological alteration in artificial neural network model of chlorophyll a: Case study of Nakdong River , 2019, Ecological Modelling.
[32] Genki Terauchi,et al. Preliminary assessment of eutrophication by remotely sensed chlorophyll-a in Toyama Bay, the Sea of Japan , 2014, Journal of Oceanography.
[33] M. McCrackin,et al. Recovery of lakes and coastal marine ecosystems from eutrophication: A global meta‐analysis , 2017 .
[34] Zhenliang Liao,et al. An optimization of artificial neural network model for predicting chlorophyll dynamics , 2017 .
[35] Salim Heddam,et al. Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA , 2016, Environmental Science and Pollution Research.
[36] Jan-Tai Kuo,et al. USING ARTIFICIAL NEURAL NETWORK FOR RESERVOIR EUTROPHICATION PREDICTION , 2007 .
[37] Qiuwen Chen,et al. Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials , 2015 .