Aquatic Living Resources

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

Juan C. Gutiérrez-Estrada and David T. Bilton
Ecological Modelling 221 (11) 1451 (2010)
DOI: 10.1016/j.ecolmodel.2010.03.007
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Modelling the factors that influence fish guilds composition using a back-propagation network: Assessment of metrics for indices of biotic integrity

Alonso Aguilar Ibarra, Muriel Gevrey, Young-Seuk Park, Puy Lim and Sovan Lek
Ecological Modelling 160 (3) 281 (2003)
DOI: 10.1016/S0304-3800(02)00259-4
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Artificial neural networks as a tool in ecological modelling, an introduction

Sovan Lek and J.F. Guégan
Ecological Modelling 120 (2-3) 65 (1999)
DOI: 10.1016/S0304-3800(99)00092-7
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Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks

Ingrid M. Schleiter, Dietrich Borchardt, Rüdiger Wagner, et al.
Ecological Modelling 120 (2-3) 271 (1999)
DOI: 10.1016/S0304-3800(99)00108-8
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Historical use, fishing management and lake characteristics explain the presence of non-native trout in Pyrenean lakes: Implications for conservation

Alexandre Miró and Marc Ventura
Biological Conservation 167 17 (2013)
DOI: 10.1016/j.biocon.2013.07.016
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Predicting local fish species richness in the garonne river basin

Sylvain Mastrorillo, Francis Dauba, Thierry Oberdorff, Jean-Francois Guégan and Sovan Lek
Comptes Rendus de l'Académie des Sciences - Series III - Sciences de la Vie 321 (5) 423 (1998)
DOI: 10.1016/S0764-4469(98)80307-7
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A Survey of Applications of Artificial Intelligence Algorithms in Eco-environmental Modelling

Kang-Suk Kim and Joon-Hong Park
Environmental Engineering Research 14 (2) 102 (2009)
DOI: 10.4491/eer.2009.14.2.102
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Estimating fish community diversity from environmental features in the Tagus estuary (Portugal): Multiple Linear Regression and Artificial Neural Network approaches

J. C. Gutiérrez-Estrada, R. Vasconcelos and M. J. Costa
Journal of Applied Ichthyology 24 (2) 150 (2008)
DOI: 10.1111/j.1439-0426.2007.01039.x
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Generalizability of artificial neural network models in ecological applications: Predicting nest occurrence and breeding success of the red-winged blackbird Agelaius phoeniceus

Uygar Özesmi, Can O. Tan, Stacy L. Özesmi and Raleigh J. Robertson
Ecological Modelling 195 (1-2) 94 (2006)
DOI: 10.1016/j.ecolmodel.2005.11.013
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Support vector machines to model presence/absence of Alburnus alburnus alborella (Teleostea, Cyprinidae) in North-Western Italy: Comparison with other machine learning techniques

Tina Tirelli, Marco Gamba and Daniela Pessani
Comptes Rendus Biologies 335 (10-11) 680 (2012)
DOI: 10.1016/j.crvi.2012.09.001
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Use of different approaches to model presence/absence of Salmo marmoratus in Piedmont (Northwestern Italy)

Tina Tirelli, Luca Pozzi and Daniela Pessani
Ecological Informatics 4 (4) 234 (2009)
DOI: 10.1016/j.ecoinf.2009.07.003
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Biologically-inspired machine learning implemented to ecological informatics

Young-Seuk Park and Tae-Soo Chon
Ecological Modelling 203 (1-2) 1 (2007)
DOI: 10.1016/j.ecolmodel.2006.05.039
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An artificial neural network approach to spatial habitat modelling with interspecific interaction

Stacy L Özesmi and Uygar Özesmi
Ecological Modelling 116 (1) 15 (1999)
DOI: 10.1016/S0304-3800(98)00149-5
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Ensembled artificial neural networks to predict the fitness score for body composition analysis

X. R. Cui, M. F. Abbod, Q. Liu, et al.
The journal of nutrition, health & aging 15 (5) 341 (2011)
DOI: 10.1007/s12603-010-0260-1
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Predictability of marine nematode biodiversity

Bea Merckx, Peter Goethals, Maaike Steyaert, et al.
Ecological Modelling 220 (11) 1449 (2009)
DOI: 10.1016/j.ecolmodel.2009.03.016
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Modeling of Cyanobacterial Blooms in Hypereutrophic Lake Dianchi, China

Guoxiang Hou, Lirong Song, Jiantong Liu, Bangding Xiao and Yongding Liu
Journal of Freshwater Ecology 19 (4) 623 (2004)
DOI: 10.1080/02705060.2004.9664743
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Modelling of Cr2N Age-precipitation in High Nitrogen Stainless Steels by Neural Networks

N. S. Reddy, I. Dzhebyan, Jae Sang Lee and Yang Mo Koo
ISIJ International 50 (2) 279 (2010)
DOI: 10.2355/isijinternational.50.279
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Catastrophic-like shifts in shallow Turkish lakes: a modeling approach

Can Ozan Tan and Meryem Beklioglu
Ecological Modelling 183 (4) 425 (2005)
DOI: 10.1016/j.ecolmodel.2004.07.033
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The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake

Sébastien Brosse, Jean-François Guegan, Jean-Nöel Tourenq and Sovan Lek
Ecological Modelling 120 (2-3) 299 (1999)
DOI: 10.1016/S0304-3800(99)00110-6
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Modeling complex nonlinear responses of shallow lakes to fish and hydrology using artificial neural networks

Can Ozan Tan and Meryem Beklioglu
Ecological Modelling 196 (1-2) 183 (2006)
DOI: 10.1016/j.ecolmodel.2006.02.003
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Prediction ability and sensitivity of artificial intelligence-based habitat preference models for predicting spatial distribution of Japanese medaka (Oryzias latipes)

Shinji Fukuda and Kazuaki Hiramatsu
Ecological Modelling 215 (4) 301 (2008)
DOI: 10.1016/j.ecolmodel.2008.03.022
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Performance comparison among multivariate and data mining approaches to model presence/absence of Austropotamobius pallipes complex in Piedmont (North Western Italy)

Tina Tirelli, Livio Favaro, Marco Gamba and Daniela Pessani
Comptes Rendus Biologies 334 (10) 695 (2011)
DOI: 10.1016/j.crvi.2011.07.002
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An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data

Julian D Olden, Michael K Joy and Russell G Death
Ecological Modelling 178 (3-4) 389 (2004)
DOI: 10.1016/j.ecolmodel.2004.03.013
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Instream physical habitat modelling types: an analysis as stream hydromorphological modelling tools for EU water resource managers

John Conallin, Eva Boegh and Jorgen Kroegsgaard Jensen
International Journal of River Basin Management 8 (1) 93 (2010)
DOI: 10.1080/15715121003715123
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Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

Stéphanie Manel, Jean-Marie Dias and Steve J. Ormerod
Ecological Modelling 120 (2-3) 337 (1999)
DOI: 10.1016/S0304-3800(99)00113-1
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Estimations of trout density and biomass: a neural networks approach

Sovan Lek and Philippe Baran
Nonlinear Analysis: Theory, Methods & Applications 30 (8) 4985 (1997)
DOI: 10.1016/S0362-546X(96)00291-X
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Predictive models in ecology: Comparison of performances and assessment of applicability

Can Ozan Tan, Uygar Özesmi, Meryem Beklioglu, Esra Per and Bahtiyar Kurt
Ecological Informatics 1 (2) 195 (2006)
DOI: 10.1016/j.ecoinf.2006.03.002
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Introducing run-size driven fisheries management for the coastal fishery of Atlantic salmon: Preseason forecasts for policy makers

Petri Suuronen and Pekka Jounela
Marine Policy 34 (3) 679 (2010)
DOI: 10.1016/j.marpol.2009.12.009
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A review of ecological models for brown trout: towards a new demogenetic model

Béatrice M. Frank, John J. Piccolo and Philippe V. Baret
Ecology of Freshwater Fish 20 (2) 167 (2011)
DOI: 10.1111/j.1600-0633.2011.00491.x
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Ecological Informatics

M. Gevrey, S. Lek and T. Oberdorff
Ecological Informatics 233 (2003)
DOI: 10.1007/978-3-662-05150-4_12
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The application of Artificial Neural Network (ANN) model to the simulation of denitrification rates in mesocosm-scale wetlands

Keunyea Song, Young-Seuk Park, Fawen Zheng and Hojeong Kang
Ecological Informatics 16 10 (2013)
DOI: 10.1016/j.ecoinf.2013.04.002
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Predicting the structure and diversity of young-of-the-year fish assemblages in large rivers

R. E. Gozlan, S. Mastrorillo, G. H. Copp and S. Lek
Freshwater Biology 41 (4) 809 (1999)
DOI: 10.1046/j.1365-2427.1999.00423.x
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Effective prediction of biodiversity in tidal flat habitats using an artificial neural network

Jae-Won Yoo, Yong-Woo Lee, Chang-Gun Lee and Chang-Soo Kim
Marine Environmental Research 83 1 (2013)
DOI: 10.1016/j.marenvres.2012.10.001
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Modelling primary production in a coastal embayment affected by upwelling using dynamic ecosystem models and artificial neural networks

Rosa M. Barciela, Emilio Garcı́a and Emilio Fernández
Ecological Modelling 120 (2-3) 199 (1999)
DOI: 10.1016/S0304-3800(99)00102-7
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Predicting fish yield of African lakes using neural networks

Raymond Laë, Sovan Lek and Jacques Moreau
Ecological Modelling 120 (2-3) 325 (1999)
DOI: 10.1016/S0304-3800(99)00112-X
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Using ecological niche models to predict the abundance and impact of invasive species: application to the common carp

Stefanie A. Kulhanek, Brian Leung and Anthony Ricciardi
Ecological Applications 21 (1) 203 (2011)
DOI: 10.1890/09-1639.1
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Methodological issues in building, training, and testing artificial neural networks in ecological applications

Stacy L. Özesmi, Can O. Tan and Uygar Özesmi
Ecological Modelling 195 (1-2) 83 (2006)
DOI: 10.1016/j.ecolmodel.2005.11.012
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A non-destructive morphometric technique to predict Ligula intestinalis L. plerocercoid load in roach (Rutilus rutilus L.) abdominal cavity

Géraldine Loot, Jean-Luc Giraudel and Sovan Lek
Ecological Modelling 156 (1) 1 (2002)
DOI: 10.1016/S0304-3800(02)00030-3
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Microsatellites and artificial neural networks: tools for the discrimination between natural and hatchery brown trout (Salmo trutta, L.) in Atlantic populations

Didier Aurelle, Sovan Lek, Jean-Luc Giraudel and Patrick Berrebi
Ecological Modelling 120 (2-3) 313 (1999)
DOI: 10.1016/S0304-3800(99)00111-8
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Consideration of fuzziness: Is it necessary in modelling fish habitat preference of Japanese medaka (Oryzias latipes)?

Shinji Fukuda
Ecological Modelling 220 (21) 2877 (2009)
DOI: 10.1016/j.ecolmodel.2008.12.025
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Short-term forecasting of halibut CPUE: Linear and non-linear univariate approaches

Ivone Alejandra Czerwinski, Juan Carlos Gutiérrez-Estrada and José Antonio Hernando-Casal
Fisheries Research 86 (2-3) 120 (2007)
DOI: 10.1016/j.fishres.2007.05.006
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A neural network for evaluating environmental impact of decoupling in rural systems

Andrea Bonfiglio
Computers, Environment and Urban Systems 35 (1) 65 (2011)
DOI: 10.1016/j.compenvurbsys.2010.06.004
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Importance of feature selection in decision-tree and artificial-neural-network ecological applications. Alburnus alburnus alborella: A practical example

Tina Tirelli and Daniela Pessani
Ecological Informatics (2010)
DOI: 10.1016/j.ecoinf.2010.11.001
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Using decision tree to develop a soil ecological quality assessment system for planning sustainable construction

Joonhong Park, Dongwon Ki, Kangsuk Kim, et al.
Expert Systems with Applications 38 (5) 5463 (2011)
DOI: 10.1016/j.eswa.2010.10.007
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Use of decision tree and artificial neural network approaches to model presence/absence ofTelestes muticellusin piedmont (North-Western Italy)

Tina Tirelli and Daniela Pessani
River Research and Applications 25 (8) 1001 (2009)
DOI: 10.1002/rra.1199
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Application Of Artificial Neural Network Models To Analyse The Relationships Between Gammarus pulex L. (Crustacea, Amphipoda) And River Characteristics

Andy P. Dedecker, Peter L. M. Goethals, Tom D'heygere, et al.
Environmental Monitoring and Assessment 111 (1-3) 223 (2005)
DOI: 10.1007/s10661-005-8221-6
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Abundance, diversity, and structure of freshwater invertebrates and fish communities: An artificial neural network approach

Sebastien Brosse, Sovan Lek and Colin R. Townsend
New Zealand Journal of Marine and Freshwater Research 35 (1) 135 (2001)
DOI: 10.1080/00288330.2001.9516983
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Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network

William A. Young, David F. Millie, Gary R. Weckman, et al.
Environmental Modelling & Software 26 (10) 1199 (2011)
DOI: 10.1016/j.envsoft.2011.04.004
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Northern bobwhite (Colinus virginianus) abundance in relation to yearly weather and long-term climate patterns

Jeffrey J Lusk, Fred S Guthery and Stephen J DeMaso
Ecological Modelling 146 (1-3) 3 (2001)
DOI: 10.1016/S0304-3800(01)00292-7
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Review and comparison of methods to study the contribution of variables in artificial neural network models

Muriel Gevrey, Ioannis Dimopoulos and Sovan Lek
Ecological Modelling 160 (3) 249 (2003)
DOI: 10.1016/S0304-3800(02)00257-0
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Abundance versus presence/absence data for modelling fish habitat preference with a genetic Takagi–Sugeno fuzzy system

Shinji Fukuda, Ans M. Mouton and Bernard De Baets
Environmental Monitoring and Assessment 184 (10) 6159 (2012)
DOI: 10.1007/s10661-011-2410-2
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Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks

Julian D Olden and Donald A Jackson
Ecological Modelling 154 (1-2) 135 (2002)
DOI: 10.1016/S0304-3800(02)00064-9
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Predictive models of collembolan diversity and abundance in a riparian habitat

Sithan Lek-Ang, Louis Deharveng and Sovan Lek
Ecological Modelling 120 (2-3) 247 (1999)
DOI: 10.1016/S0304-3800(99)00106-4
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Extracting the contribution of independent variables in neural network models: a new approach to handle instability

Juan de Oña and Concepción Garrido
Neural Computing and Applications 25 (3-4) 859 (2014)
DOI: 10.1007/s00521-014-1573-5
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Synthesis, in vitro evaluation and QSAR modelling of potential antitumoral 3,4-dihydropyrimidin-2-(1H)-thiones

Mariana Matias, Gonçalo Campos, Adriana O. Santos, et al.
Arabian Journal of Chemistry 12 (8) 5086 (2019)
DOI: 10.1016/j.arabjc.2016.12.007
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Modelling habitat requirements of white-clawed crayfish (Austropotamobius pallipes) using support vector machines

L. Favaro, T. Tirelli and D. Pessani
Knowledge and Management of Aquatic Ecosystems (401) 21 (2011)
DOI: 10.1051/kmae/2011037
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Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids

Petar Žuvela, Jonathan David and Ming Wah Wong
Journal of Computational Chemistry 39 (16) 953 (2018)
DOI: 10.1002/jcc.25168
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Ecological informatics as an advanced interdisciplinary interpretation of ecosystems

Tae-Soo Chon and Young-Seuk Park
Ecological Informatics 1 (3) 213 (2006)
DOI: 10.1016/j.ecoinf.2006.02.007
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Modelling of microhabitat used by fish in natural and regulated flows in the river Garonne (France)

Yorick Reyjol, Puy Lim, Alain Belaud and Sovan Lek
Ecological Modelling 146 (1-3) 131 (2001)
DOI: 10.1016/S0304-3800(01)00301-5
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Potential antitumoral 3,4-dihydropyrimidin-2-(1H)-ones: synthesis, in vitro biological evaluation and QSAR studies

Mariana Matias, Gonçalo Campos, Adriana O. Santos, et al.
RSC Advances 6 (88) 84943 (2016)
DOI: 10.1039/C6RA14596E
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Adoption of Machine Learning Techniques in Ecology and Earth Science

Anne Thessen
One Ecosystem 1 e8621 (2016)
DOI: 10.3897/oneeco.1.e8621
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GRAPHICAL REPRESENTATION OF CAUSE-EFFECT RELATIONSHIPS AMONG CHEMICAL PROCESS VARIABLES USING A NEURAL NETWORK APPROACH

GUSTAVO M. DE ALMEIDA, MARCELO CARDOSO, DANILO C. RENA and SONG W. PARK
International Journal of Computational Intelligence and Applications 09 (01) 69 (2010)
DOI: 10.1142/S146902681000277X
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Effects of sample size and network depth on a deep learning approach to species distribution modeling

Donald J. Benkendorf and Charles P. Hawkins
Ecological Informatics 60 101137 (2020)
DOI: 10.1016/j.ecoinf.2020.101137
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Interpretation of Bayesian Neural Networks for Predicting the Duration of Detected Incidents

Hyoshin Park, Ali Haghani and Xin Zhang
Journal of Intelligent Transportation Systems 00 (2015)
DOI: 10.1080/15472450.2015.1082428
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Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast

Shima Shamkhali Chenar and Zhiqiang Deng
Environment International 111 212 (2018)
DOI: 10.1016/j.envint.2017.11.032
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Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption

Ali Soofastaei, Saiied M. Aminossadati, Mohammad M. Arefi and Mehmet S. Kizil
International Journal of Mining Science and Technology 26 (2) 285 (2016)
DOI: 10.1016/j.ijmst.2015.12.015
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Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass

Mohammad Hasan Shojaeefard, Mostafa Akbari, Mojtaba Tahani and Foad Farhani
Advances in Materials Science and Engineering 2013 1 (2013)
DOI: 10.1155/2013/574914
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A State-Based Sensitivity Analysis for Distinguishing the Global Importance of Predictor Variables in Artificial Neural Networks

Ehsan Ardjmand, David F. Millie, Iman Ghalehkhondabi, William A. Young II and Gary R. Weckman
Advances in Artificial Neural Systems 2016 1 (2016)
DOI: 10.1155/2016/2303181
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Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis

Madhavi Anushka Elangasinghe, Naresh Singhal, Kim N. Dirks and Jennifer A. Salmond
Atmospheric Pollution Research 5 (4) 696 (2014)
DOI: 10.5094/APR.2014.079
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Application of an artificial neural network (ANN) model for predicting mosquito abundances in urban areas

Keun Young Lee, Namil Chung and Suntae Hwang
Ecological Informatics 36 172 (2016)
DOI: 10.1016/j.ecoinf.2015.08.011
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Environmental indicators of oyster norovirus outbreaks in coastal waters

Shima Shamkhali Chenar and Zhiqiang Deng
Marine Environmental Research 130 275 (2017)
DOI: 10.1016/j.marenvres.2017.08.009
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Artificial neural networks applied to port operability assessment

I. López, M. López and G. Iglesias
Ocean Engineering 109 298 (2015)
DOI: 10.1016/j.oceaneng.2015.09.016
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Towards a physiological response of fishes under variable environmental conditions: An approach through neural network

Joyita Mukherjee, Mahammed Moniruzzaman, Suman Bhusan Chakraborty, Sovan Lek and Santanu Ray
Ecological Indicators 78 381 (2017)
DOI: 10.1016/j.ecolind.2017.03.038
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State-dependent asset allocation using neural networks

Reza Bradrania and Davood Pirayesh Neghab
The European Journal of Finance 1 (2021)
DOI: 10.1080/1351847X.2021.1960404
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A non-iterative method for pruning hidden neurons in neural networks with random weights

Pablo A. Henríquez and Gonzalo A. Ruz
Applied Soft Computing 70 1109 (2018)
DOI: 10.1016/j.asoc.2018.03.013
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Identifying the relative importance of predictive variables in artificial neural networks based on data produced through a discrete event simulation of a manufacturing environment

R. Pires dos Santos, D. L. Dean, J. M. Weaver and Y. Hovanski
International Journal of Modelling and Simulation 39 (4) 234 (2019)
DOI: 10.1080/02286203.2018.1558736
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Detection of potential fishing zones for neon flying squid based on remote-sensing data in the Northwest Pacific Ocean using an artificial neural network

Jintao Wang, Wei Yu, Xinjun Chen, Lin Lei and Yong Chen
International Journal of Remote Sensing 36 (13) 3317 (2015)
DOI: 10.1080/01431161.2015.1042121
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Predicting presence and absence of trout (Salmo trutta) in Iran

Hossein Mostafavi, Florian Pletterbauer, Brian W. Coad, et al.
Limnologica 46 1 (2014)
DOI: 10.1016/j.limno.2013.12.001
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Modelling roach (Rutilus rutilus) microhabitat using linear and nonlinear techniques

Sebastien Brosse and Sovan Lek
Freshwater Biology 44 (3) 441 (2000)
DOI: 10.1046/j.1365-2427.2000.00580.x
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Spatial organisation of European eel (Anguilla anguilla L.) in a small catchment

P Laffaille, E Feunteun, A Baisez, et al.
Ecology of Freshwater Fish 12 (4) 254 (2003)
DOI: 10.1046/j.1600-0633.2003.00021.x
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Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future

Pavel Polishchuk
Journal of Chemical Information and Modeling 57 (11) 2618 (2017)
DOI: 10.1021/acs.jcim.7b00274
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Analysis of macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection procedure with artificial neural networks

Wim Gabriels, Peter L. M. Goethals, Andy P. Dedecker, Sovan Lek and Niels De Pauw
Aquatic Ecology 41 (3) 427 (2007)
DOI: 10.1007/s10452-007-9081-7
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Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Peter L. M. Goethals, Andy P. Dedecker, Wim Gabriels, Sovan Lek and Niels De Pauw
Aquatic Ecology 41 (3) 491 (2007)
DOI: 10.1007/s10452-007-9093-3
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An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models

Simone Franceschini, Lorenzo Tancioni, Massimo Lorenzoni, et al.
PLOS ONE 14 (1) e0211445 (2019)
DOI: 10.1371/journal.pone.0211445
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Ecological Informatics

M. Gevrey, S. Lek and T. Oberdorff
Ecological Informatics 293 (2006)
DOI: 10.1007/3-540-28426-5_14
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Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

Ans M. Mouton, Andy P. Dedecker, Sovan Lek and Peter L. M. Goethals
Environmental Modeling & Assessment 15 (1) 65 (2010)
DOI: 10.1007/s10666-009-9192-8
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Environmental factors as indicators of dissolved oxygen concentration and zooplankton abundance: Deep learning versus traditional regression approach

Arnab Banerjee, Moitreyee Chakrabarty, Nabyendu Rakshit, Amiya Ranjan Bhowmick and Santanu Ray
Ecological Indicators 100 99 (2019)
DOI: 10.1016/j.ecolind.2018.09.051
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Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information

Simone Franceschini, Emanuele Gandola, Marco Martinoli, Lorenzo Tancioni and Michele Scardi
Scientific Reports 8 (1) (2018)
DOI: 10.1038/s41598-018-22761-4
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Artificial Neuronal Networks

S. Lek, J. L. Giraudel and J. F. Guégan
Artificial Neuronal Networks 3 (2000)
DOI: 10.1007/978-3-642-57030-8_1
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Environmental impact prediction using neural network modelling. An example in wildlife damage

François Spitz and Sovan Lek
Journal of Applied Ecology 36 (2) 317 (1999)
DOI: 10.1046/j.1365-2664.1999.00400.x
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Assessing the Ecological Integrity of Running Waters

R. Wagner, T. Dapper and H.-H. Schmidt
Assessing the Ecological Integrity of Running Waters 143 (2000)
DOI: 10.1007/978-94-011-4164-2_11
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Eel Biology

Eric Feunteun, Pascal Laffaille, Tony Robinet, et al.
Eel Biology 191 (2003)
DOI: 10.1007/978-4-431-65907-5_14
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Anthropogenic and habitat correlates of hybridization between hatchery and wild brook trout

Andrew B. Harbicht, Mohammed Alshamlih, Chris C. Wilson, Dylan J. Fraser and Paloma Morán
Canadian Journal of Fisheries and Aquatic Sciences 71 (5) 688 (2014)
DOI: 10.1139/cjfas-2013-0460
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Embedding ecological knowledge into artificial neural network training: A marine phytoplankton primary production model case study

F. Mattei and M. Scardi
Ecological Modelling 421 108985 (2020)
DOI: 10.1016/j.ecolmodel.2020.108985
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Is the Atlantic surface temperature a good proxy for forecasting the recruitment of European eel in the Guadalquivir estuary?

Juan Carlos Gutiérrez-Estrada and Inmaculada Pulido-Calvo
Progress in Oceanography 130 112 (2015)
DOI: 10.1016/j.pocean.2014.10.007
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Advanced Metaheuristic Methods in Big Data Retrieval and Analytics

Mohamed Elhadi Rahmani and Abdelmalek Amine
Advances in Computational Intelligence and Robotics, Advanced Metaheuristic Methods in Big Data Retrieval and Analytics 27 (2019)
DOI: 10.4018/978-1-5225-7338-8.ch002
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Updating the neural network sediment load models using different sensitivity analysis methods: a regional application

Abbas Abbaszadeh Shahri, Mojtaba Saneie, Seyed Abbas Hosseini and Reza Asheghi
Journal of Hydroinformatics 22 (3) 562 (2020)
DOI: 10.2166/hydro.2020.098
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A depth-resolved artificial neural network model of marine phytoplankton primary production

F. Mattei, S. Franceschini and M. Scardi
Ecological Modelling 382 51 (2018)
DOI: 10.1016/j.ecolmodel.2018.05.003
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Deep data analysis for aspiration pressure estimation in a high-pressure gas atomization process using an artificial neural network

Rashed Kaiser, Songkil Kim and Donggeun Lee
Chemical Engineering and Processing - Process Intensification 153 107924 (2020)
DOI: 10.1016/j.cep.2020.107924
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