Large-scale prediction of tropical stream water quality using Rough Sets Theory

[1]  M. Ribeiro,et al.  Untangling associations between chironomid taxa in Neotropical streams using local and landscape filters , 2010 .

[2]  Jianming Zhan,et al.  Covering-based variable precision fuzzy rough sets with PROMETHEE-EDAS methods , 2020, Inf. Sci..

[3]  Xu Guo-zhen,et al.  Application of dynamic fuzzy neural networks based on EBF to multifactorial flooding index prediction , 2013, Proceedings of the 32nd Chinese Control Conference.

[4]  M. Feio,et al.  Testing a Multiple Machine Learning Tool (HYDRA) for the Bioassessment of Fresh Waters , 2014, Freshwater Science.

[5]  F. Valente‐Neto,et al.  Selecting indicators based on biodiversity surrogacy and environmental response in a riverine network: Bringing operationality to biomonitoring , 2018, Ecological Indicators.

[6]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[7]  Tae-Soo Chon,et al.  Stream biomonitoring using macroinvertebrates around the globe: a comparison of large-scale programs , 2014, Environmental Monitoring and Assessment.

[8]  M. Fortin,et al.  The Brazilian Atlantic Forest: A Shrinking Biodiversity Hotspot , 2011 .

[9]  João Carlos de Castro Pena,et al.  Nonlinear responses in damselfly community along a gradient of habitat loss in a savanna landscape , 2016 .

[10]  L. François,et al.  Contrasting climate risks predicted by dynamic vegetation and ecological niche-based models applied to tree species in the Brazilian Atlantic Forest , 2018, Regional Environmental Change.

[11]  P. Harrison,et al.  Linkages between biodiversity attributes and ecosystem services: A systematic review , 2014 .

[12]  V. Resh,et al.  Species at Risk (SPEAR) index indicates effects of insecticides on stream invertebrate communities in soy production regions of the Argentine Pampas. , 2017, The Science of the total environment.

[13]  M. Galetti,et al.  Mammal defaunation as surrogate of trophic cascades in a biodiversity hotspot , 2013 .

[14]  P. McIntyre,et al.  Global threats to human water security and river biodiversity , 2010, Nature.

[15]  T. Siqueira,et al.  The taxonomic distinctness of macroinvertebrate communities of Atlantic Forest streams cannot be predicted by landscape and climate variables, but traditional biodiversity indices can. , 2014, Brazilian journal of biology = Revista brasleira de biologia.

[16]  D. Dudgeon Prospects for sustaining freshwater biodiversity in the 21st century: linking ecosystem structure and function , 2010 .

[17]  M. Spies,et al.  Inventory of caddisflies (Trichoptera: Insecta) of the Campos do Jordão State Park, São Paulo State, Brazil , 2009 .

[18]  G. Likens,et al.  Moving Headwater Streams to the Head of the Class , 2005 .

[19]  Jurgita Antucheviciene,et al.  Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature , 2017, Complex..

[20]  Taylor H. Ricketts,et al.  The Convention on Biological Diversity's 2010 Target , 2005, Science.

[21]  Andrzej Skowron,et al.  Rough Sets: A Tutorial , 1998 .

[22]  J. Heino A macroecological perspective of diversity patterns in the freshwater realm , 2011 .

[23]  L. M. Bini,et al.  A Metacommunity Framework for Enhancing the Effectiveness of Biological Monitoring Strategies , 2012, PloS one.

[24]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[25]  Jianming Zhan,et al.  Two types of coverings based multigranulation rough fuzzy sets and applications to decision making , 2018, Artificial Intelligence Review.

[26]  Harry Biggs,et al.  Integrating indicators, endpoints and value systems in strategic management of the rivers of the Kruger National Park , 1999 .

[27]  George E. Host,et al.  Recent developments in landscape approaches for the study of aquatic ecosystems , 2010, Journal of the North American Benthological Society.

[28]  David G Angeler,et al.  A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels , 2015, Ecology and evolution.

[29]  Peace Liz Sasha Musonge,et al.  Bayesian belief network models to analyse and predict ecological water quality in rivers , 2015 .

[30]  Development of a benthic multimetric index for the Serra da Bocaina bioregion in Southeast Brazil. , 2013, Brazilian journal of biology = Revista brasleira de biologia.

[31]  David W. Scott,et al.  Sturges' rule , 2009 .

[32]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[33]  R. Alves,et al.  Richness and distribution of Ephemeroptera, Plecoptera and Trichoptera in Atlantic forest streams , 2019, Acta Oecologica.

[34]  Zdzis?aw Pawlak,et al.  Rough sets , 2005, International Journal of Computer & Information Sciences.

[35]  J. Allan Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems , 2004 .

[36]  J. Heino,et al.  Weak relationships between landscape characteristics and multiple facets of stream macroinvertebrate biodiversity in a boreal drainage basin , 2008, Landscape Ecology.

[37]  Christopher B Anderson,et al.  Biodiversity monitoring, earth observations and the ecology of scale. , 2018, Ecology letters.

[38]  Maryam Zavareh,et al.  Application of Rough Set Theory to Water Quality Analysis: A Case Study , 2018, Data.

[39]  S. Trivinho-Strixino,et al.  Concordance between macroinvertebrate communities and the typological classification of white and clear-water streams in Western Brazilian Amazonia , 2012 .

[40]  Robert M. Hughes,et al.  Defining quantitative stream disturbance gradients and the additive role of habitat variation to explain macroinvertebrate taxa richness , 2013 .

[41]  C. Swan,et al.  Toward a practical use of Neotropical odonates as bioindicators: Testing congruence across taxonomic resolution and life stages , 2016 .

[42]  J. Vitule,et al.  Climate change as a driver of biotic homogenization of woody plants in the Atlantic Forest , 2018 .

[43]  A. S. Melo Effects of taxonomic and numeric resolution on the ability to detect ecological patterns at a local scale using stream macroinvertebrates , 2005 .

[44]  A. E. Siegloch,et al.  Diversity of Ephemeroptera (Insecta) of the Serra da Mantiqueira and Serra do Mar, southeastern Brazil , 2012 .

[45]  Maria João Feio,et al.  Predictive Models for Freshwater Biological Assessment: Statistical Approaches, Biological Elements and the Iberian Peninsula Experience: A Review , 2011 .

[46]  Jiye Liang,et al.  Hesitant fuzzy linguistic rough set over two universes model and its applications , 2018, Int. J. Mach. Learn. Cybern..

[47]  R. Hughes,et al.  Partitioning taxonomic diversity of aquatic insect assemblages and functional feeding groups in neotropical savanna headwater streams. , 2017, Ecological indicators.

[48]  R. Naiman,et al.  Freshwater biodiversity: importance, threats, status and conservation challenges , 2006, Biological reviews of the Cambridge Philosophical Society.

[49]  J. Nessimian,et al.  A multimetric index based on benthic macroinvertebrates for evaluation of Atlantic Forest streams at Rio de Janeiro State, Brazil , 2006, Hydrobiologia.

[50]  Haruna Chiroma,et al.  Machine learning for email spam filtering: review, approaches and open research problems , 2019, Heliyon.

[51]  T. Siqueira,et al.  Using environmental and spatial filters to explain stonefly occurrences in southeastern Brazilian streams: implications for biomonitoring Utilizando filtros ambientais e espaciais para explicar a ocorrência de plecópteros em córregos do sudeste Brasileiro: implicações para o biomonitoramento , 2008 .

[52]  Ricardo Ojima,et al.  Resgates sobre população e ambiente: breve análise da dinâmica demográfica e a urbanização nos biomas brasileiros , 2013 .

[53]  Jianming Zhan,et al.  A novel decision-making approach based on three-way decisions in fuzzy information systems , 2020, Inf. Sci..

[54]  Incorporating natural variability in the bioassessment of stream condition in the Atlantic Forest biome, Brazil , 2016 .

[55]  Guoyin Wang,et al.  A survey on rough set theory and its applications , 2016, CAAI Trans. Intell. Technol..

[56]  Jiye Liang,et al.  Multi-granularity three-way decisions with adjustable hesitant fuzzy linguistic multigranulation decision-theoretic rough sets over two universes , 2020, Inf. Sci..

[57]  Jan Komorowski,et al.  Rough Sets in Bioinformatics , 2007, Trans. Rough Sets.

[58]  B. Statzner,et al.  Developments in aquatic insect biomonitoring: a comparative analysis of recent approaches. , 2006, Annual review of entomology.

[59]  F. O. Roque,et al.  Choice of macroinvertebrate metrics to evaluate stream conditions in Atlantic Forest, Brazil , 2011, Environmental monitoring and assessment.

[60]  Ping-Feng Pai,et al.  A Rough Set Based Model in Water Quality Analysis , 2010 .

[61]  F. Altermatt,et al.  Nonlinear higher order abiotic interactions explain riverine biodiversity , 2018 .

[62]  Minar Naomi Damanik-Ambarita,et al.  Ecological Models to Infer the Quantitative Relationship between Land Use and the Aquatic Macroinvertebrate Community , 2018 .

[63]  Sovan Lek,et al.  Applications of artificial neural networks predicting macroinvertebrates in freshwaters , 2007, Aquatic Ecology.