Articles citing this paper
Improved estimation, using neural networks, of the food consumption of fish populations
S Lek, A Belaud, I Dimopoulos, J Lauga and J Moreau
46(8) pp.1229 - 1236
69 articles found in Crossref database.
Neural networks for analyzing service quality in public transportation
Garrido Concepción,
de Oña Rocío, de Oña Juan
Expert Systems with Applications. 2014 41(15). p.6830
Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment
Sameen Maher Ibrahim,
Pradhan Biswajeet, Lee Saro
CATENA. 2020 186 p.104249
Ensembled artificial neural networks to predict the fitness score for body composition analysis
Cui X.R.,
Abbod M.F.,
Liu Q.,
Shieh Jiann-Shing,
Chao T.Y.,
Hsieh C.Y., Yang Y.C.
The Journal of nutrition, health and aging. 2011 15(5). p.341
Research on pointing correction algorithm of laser ranging telescope oriented to space debris
Ma Tianming,
Zhao Chunmei, He Zhengbin
Journal of Laser Applications. 2020 32(1).
Extracting the contribution of independent variables in neural network models: a new approach to handle instability
de Oña Juan, Garrido Concepción
Neural Computing and Applications. 2014 25(3-4). p.859
An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions
Chu Haibo,
Wu Wenyan,
Wang Q.J.,
Nathan Rory, Wei Jiahua
Environmental Modelling & Software. 2020 124 p.104587
Applications of artificial neural networks predicting macroinvertebrates in freshwaters
Goethals Peter L. M.,
Dedecker Andy P.,
Gabriels Wim,
Lek Sovan, De Pauw Niels
Aquatic Ecology. 2007 41(3). p.491
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
Razavi Saman,
Jakeman Anthony,
Saltelli Andrea,
Prieur Clémentine,
Iooss Bertrand,
Borgonovo Emanuele,
Plischke Elmar,
Lo Piano Samuele,
Iwanaga Takuya,
Becker William,
Tarantola Stefano,
Guillaume Joseph H.A.,
Jakeman John,
Gupta Hoshin,
Melillo Nicola,
Rabitti Giovanni,
Chabridon Vincent,
Duan Qingyun,
Sun Xifu,
Smith Stefán,
Sheikholeslami Razi,
Hosseini Nasim,
Asadzadeh Masoud,
Puy Arnald,
Kucherenko Sergei, Maier Holger R.
Environmental Modelling & Software. 2021 137 p.104954
Modelling of Cr2N Age-precipitation in High Nitrogen Stainless Steels by Neural Networks
Reddy N. S.,
Dzhebyan I.,
Lee Jae Sang, Koo Yang Mo
ISIJ International. 2010 50(2). p.279
Assessment of stream biological responses under multiple-stress conditions
Comte Lise,
Lek Sovan,
de Deckere Eric,
de Zwart Dick, Gevrey Muriel
Environmental Science and Pollution Research. 2010 17(8). p.1469
Neural network and fuzzy logic models for pacific halibut recruitment analysis
Chen Ding-Geng, Hare Steven R.
Ecological Modelling. 2006 195(1-2). p.11
Application of neural networks to model changes in fish community biomass in relation to pressure indicators and comparison with a linear approach
Dempsey Danielle P.,
Pepin Pierre,
Koen-Alonso Mariano, Gentleman Wendy C.
Canadian Journal of Fisheries and Aquatic Sciences. 2020 77(6). p.963
Age at maturation predicted from routine scale measurements in Norwegian spring-spawning herring (Clupea harengus) using discriminant and neural network analyses
Engelhard Georg H,
Dieckmann Ulf, Godø Olav Rune
ICES Journal of Marine Science. 2003 60(2). p.304
GRAPHICAL REPRESENTATION OF CAUSE-EFFECT RELATIONSHIPS AMONG CHEMICAL PROCESS VARIABLES USING A NEURAL NETWORK APPROACH
DE ALMEIDA GUSTAVO M.,
CARDOSO MARCELO,
RENA DANILO C., PARK SONG W.
International Journal of Computational Intelligence and Applications. 2010 09(01). p.69
A neural network for evaluating environmental impact of decoupling in rural systems
Bonfiglio Andrea
Computers, Environment and Urban Systems. 2011 35(1). p.65
Ecological Informatics (2006)
The application of artificial neural networks in plant protection*
Peacock L.,
Worner S., Pitt J.
EPPO Bulletin. 2007 37(2). p.277
Ecological Model Types (2016)
Applications of the self-organising feature map neural network in community data analysis
Foody Giles M.
Ecological Modelling. 1999 120(2-3). p.97
Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling
Li Xian,
Shu Longcang,
Liu Lihong,
Yin Dan, Wen Jinmei
Hydrogeology Journal. 2012 20(4). p.727
A neural network model for forecasting fish stock recruitment
Chen D G, Ware D M
Canadian Journal of Fisheries and Aquatic Sciences. 1999 56(12). p.2385
Models for estimating feed intake in aquaculture: A review
Sun Min,
Hassan Shahbaz G., Li Daoliang
Computers and Electronics in Agriculture. 2016 127 p.425
Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids
Žuvela Petar,
David Jonathan, Wong Ming Wah
Journal of Computational Chemistry. 2018 39(16). p.953
Modelling the influence of environmental parameters over marine planktonic microbial communities using artificial neural networks
Coutinho F.H.,
Thompson C.C.,
Cabral A.S.,
Paranhos R.,
Dutilh B.E., Thompson F.L.
Science of The Total Environment. 2019 677 p.205
Ecological informatics as an advanced interdisciplinary interpretation of ecosystems
Chon Tae-Soo, Park Young-Seuk
Ecological Informatics. 2006 1(3). p.213
Using artificial neural networks to assess wolf distribution patterns in Portugal
Bessa‐Gomes Carmen, Petrucci‐Fonseca Francisco
Animal Conservation. 2003 6(3). p.221
Application Of Artificial Neural Network Models To Analyse The Relationships Between Gammarus pulex L. (Crustacea, Amphipoda) And River Characteristics
Dedecker Andy P.,
Goethals Peter L. M.,
D'heygere Tom,
Gevrey Muriel,
Lek Sovan, De Pauw Niels
Environmental Monitoring and Assessment. 2005 111(1-3). p.223
Ecological Informatics (2003)
Advanced Modelling Techniques Studying Global Changes in Environmental Sciences (2015)
Illumination of parameter contributions on uneven break phenomenon in underground stoping mines
Jang Hyongdoo,
Topal Erkan, Kawamura Youhei
International Journal of Mining Science and Technology. 2016 26(6). p.1095
Biologically-inspired machine learning implemented to ecological informatics
Park Young-Seuk, Chon Tae-Soo
Ecological Modelling. 2007 203(1-2). p.1
Artificial Neuronal Networks (2000)
Analysis of macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection procedure with artificial neural networks
Gabriels Wim,
Goethals Peter L. M.,
Dedecker Andy P.,
Lek Sovan, De Pauw Niels
Aquatic Ecology. 2007 41(3). p.427
Artificial neural networks as a classification method in the behavioural sciences
Reby David,
Lek Sovan,
Dimopoulos Ioannis,
Joachim Jean,
Lauga Jacques, Aulagnier Stéphane
Behavioural Processes. 1997 40(1). p.35
Review and comparison of methods to study the contribution of variables in artificial neural network models
Gevrey Muriel,
Dimopoulos Ioannis, Lek Sovan
Ecological Modelling. 2003 160(3). p.249
Diffuse large B-cell lymphoma classification using linguistic analysis and ensembled artificial neural networks
Cui Xingran,
Lin Chung-Wu,
Abbod Maysam F.,
Liu Quan, Shieh Jiann-Shing
Journal of the Taiwan Institute of Chemical Engineers. 2012 43(1). p.15
Effective prediction of biodiversity in tidal flat habitats using an artificial neural network
Yoo Jae-Won,
Lee Yong-Woo,
Lee Chang-Gun, Kim Chang-Soo
Marine Environmental Research. 2013 83 p.1
Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast
Chenar Shima Shamkhali, Deng Zhiqiang
Environment International. 2018 111 p.212
Assessing the Role of Environmental Factors on Baltic Cod Recruitment, a Complex Adaptive System Emergent Property
Krekoukiotis Dionysis,
Palacz Artur Piotr, St. John Michael A.
Frontiers in Marine Science. 2016 3
Predicting fish species richness and abundance in the Lower Mekong Basin
Ngor Peng Bun,
Uy Sophorn,
Sor Ratha,
Chan Bunyeth,
Holway Joseph,
Null Sarah E.,
So Nam,
Grenouillet Gaël,
Chandra Sudeep,
Hogan Zeb S., Lek Sovan
Frontiers in Ecology and Evolution. 2023 11
Supply requirement prediction during long duration space missions using Bayesian estimation
Held Jason,
Wynn Louise,
Reed Judd, Wang Rosalind
International Journal of Logistics Research and Applications. 2007 10(4). p.351
Dioxin emission modeling using feature selection and simplified DFR with residual error fitting for the grate-based MSWI process
Xia Heng,
Tang Jian,
Aljerf Loai,
Cui Canlin,
Gao Bingyin, Ukaogo Prince Onyedinma
Waste Management. 2023 168 p.256
Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model
Golshani Nima,
Shabanpour Ramin,
Mahmoudifard Seyed Mehdi,
Derrible Sybil, Mohammadian Abolfazl
Travel Behaviour and Society. 2018 10 p.21
Water quality assessment using diatom assemblages and advanced modelling techniques
Gevrey Muriel,
Rimet Frédéric,
Park Young Seuk,
Giraudel Jean‐Luc,
Ector Luc, Lek Sovan
Freshwater Biology. 2004 49(2). p.208
Severity Prediction of Traffic Accidents with Recurrent Neural Networks
Sameen Maher, Pradhan Biswajeet
Applied Sciences. 2017 7(6). p.476
Environmental indicators of oyster norovirus outbreaks in coastal waters
Shamkhali Chenar Shima, Deng Zhiqiang
Marine Environmental Research. 2017 130 p.275
Modelling spatial distribution of hard bottom benthic communities and their functional response to environmental parameters
Bandelj Vinko,
Curiel Daniele,
Lek Sovan,
Rismondo Andrea, Solidoro Cosimo
Ecological Modelling. 2009 220(21). p.2838
Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models
Mouton Ans M.,
Dedecker Andy P.,
Lek Sovan, Goethals Peter L. M.
Environmental Modeling & Assessment. 2010 15(1). p.65
The application of Artificial Neural Network (ANN) model to the simulation of denitrification rates in mesocosm-scale wetlands
Song Keunyea,
Park Young-Seuk,
Zheng Fawen, Kang Hojeong
Ecological Informatics. 2013 16 p.10
Predicting fish yield of African lakes using neural networks
Laë Raymond,
Lek Sovan, Moreau Jacques
Ecological Modelling. 1999 120(2-3). p.325
Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters
Park Young-Seuk,
Céréghino Régis,
Compin Arthur, Lek Sovan
Ecological Modelling. 2003 160(3). p.265
Machine Learning and Artificial Intelligence in Marketing and Sales (2021)
Predictive models of collembolan diversity and abundance in a riparian habitat
Lek-Ang Sithan,
Deharveng Louis, Lek Sovan
Ecological Modelling. 1999 120(2-3). p.247
Simplifying artificial neural network models of river basin behaviour by an automated procedure for input variable selection
Oliveira Guilherme G.,
Pedrollo Olavo C., Castro Nilza M.R.
Engineering Applications of Artificial Intelligence. 2015 40 p.47
Using artificial neural networks to model the suitability of coastline for breeding by New Zealand fur seals (Arctocephalus forsteri)
Bradshaw Corey J.A,
Davis Lloyd S,
Purvis Martin,
Zhou Qingqing, Benwell George L
Ecological Modelling. 2002 148(2). p.111
Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors
Lee Sanghoon,
Lee Kang-Kun, Yoon Heesung
Hydrogeology Journal. 2019 27(2). p.567
Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data
Srivastava Prashant K.,
Gupta Manika,
Singh Ujjwal,
Prasad Rajendra,
Pandey Prem Chandra,
Raghubanshi A. S., Petropoulos George P.
Environment, Development and Sustainability. 2021 23(4). p.5504
Predictability of marine nematode biodiversity
Merckx Bea,
Goethals Peter,
Steyaert Maaike,
Vanreusel Ann,
Vincx Magda, Vanaverbeke Jan
Ecological Modelling. 2009 220(11). p.1449
Extraction of attribute importance from satisfaction surveys with data mining techniques: a comparison between neural networks and decision trees
de Oña Juan,
de Oña Rocío, Garrido Concepción
Transportation Letters. 2017 9(1). p.39
A classification-based deep belief networks model framework for daily streamflow forecasting
Chu Haibo,
Wei Jiahua,
Wu Wenyan,
Jiang Yuan,
Chu Qi, Meng Xiujing
Journal of Hydrology. 2021 595 p.125967
Stochastic models that predict trout population density or biomass on a mesohabitat scale
Baran Philippe,
Lek Sovan,
Delacoste Marc, Belaud Alain
Hydrobiologia. 1996 337(1-3). p.1
Improved validation framework and R-package for artificial neural network models
Humphrey Greer B.,
Maier Holger R.,
Wu Wenyan,
Mount Nick J.,
Dandy Graeme C.,
Abrahart Robert J., Dawson Christian W.
Environmental Modelling & Software. 2017 92 p.82
Ensembled artificial neural networks for diffuse large B-cell lymphoma classification
The 2nd International Conference on Information Science and Engineering (2010)
A fuzzy logic model with genetic algorithm for analyzing fish stock-recruitment relationships
Chen D G,
Hargreaves N B,
Ware D M, Liu Y
Canadian Journal of Fisheries and Aquatic Sciences. 2000 57(9). p.1878
Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019 (2020)
Sensitivity analysis and stability patterns of two-species pest models using artificial neural networks
Park Young-Seuk,
Rabinovich Jorge, Lek Sovan
Ecological Modelling. 2007 204(3-4). p.427
EVALUATION OF ENVIRONMENTAL FACTORS ON CYANOBACTERIAL BLOOM IN EUTROPHIC RESERVOIR USING ARTIFICIAL NEURAL NETWORKS1
Ahn Chi-Yong,
Oh Hee-Mock, Park Young-Seuk
Journal of Phycology. 2011 47(3). p.495
Application of Artificial Neural Networks for Noise Barrier Optimization
Zannin Paulo Henrique Trombetta,
Do Nascimento Eriberto Oliveira,
Da Paz Elaine Carvalho, Do Valle Felipe
Environments. 2018 5(12). p.135
Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks
Olden Julian D, Jackson Donald A
Ecological Modelling. 2002 154(1-2). p.135