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heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and humaninduced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.
rough set theory  water quality  attribute reduction  core attribute  rule extraction  climate extreme indices (CEIs)  ClimPACT  GLDAS  Expert Team on Climate Change Detection and Indices (ETCCDI)  Expert Team on Sectorspecific Climate Indices (ETSCI)  Dataset Licensedatabase  geophysical monitoring  magnetotelluric monitoring  processing  arthropod vector  invasive species  microhabitat  species distribution modeling  remote sensing  data assimilation  3DVar  multiclass classification  soil texture calculator  kNearest Neighbors  support vector machines  decision trees  ensemble learning  earthscience data  data scarcity  missing data  data quality  data imputation  statistical methods  machine learning  environmental modeling  environmental observations
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This book brings together the latest research results of air quality assessment standards and sustainable development in developing countries. The content is full and the discussion is vivid. These articles are suitable for students and researchers at all levels seeking to understand the status of air pollution, governance standards, and governance effects in developing countries.
relevance analysis  spatial and temporal distribution characteristics  PM2.5  Beijing  environmental targetsetting  performance  hierarchical linear model  environmental governance  China  air quality  air quality evaluation standards  AQI  Jiangsu province  fuzzy comprehensive evaluation  emission inventory  livestock  greenhouse gases  air pollutant  vehicle  pollution  measurement and environment  whistleblowing  air pollution  evolutionary game  environmental supervision  AQI indicators  air pollution  collaborative filtering  BeijingTianjinHebei region  PM2.5 concentrations  functional principal component analysis  adaptive clustering analysis  functional ANOVA  spatial and temporal difference  entropy weight method  fuzzy optimization model  air quality  primary pollutants  air quality  comprehensive pollution index analysis  grey correlation analysis  Euclid approach degree method  PSR Model  rough set  entropy weight method  attribute reduction  haze  linear timevarying GM(1,N) model  interval grey number  Beijing  forecasting  sustainable development  wind power development  carbon emissions
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Some of the most beautiful studies in Mathematics are related to Symmetry and Geometry. For this reason, we select here some contributions about such aspects and Discrete Geometry. As we know, Symmetry in a system means invariance of its elements under conditions of transformations. When we consider network structures, symmetry means invariance of adjacency of nodes under the permutations of node set. The graph isomorphism is an equivalence relation on the set of graphs. Therefore, it partitions the class of all graphs into equivalence classes. The underlying idea of isomorphism is that some objects have the same structure if we omit the individual character of their components. A set of graphs isomorphic to each other is denominated as an isomorphism class of graphs. The automorphism of a graph will be an isomorphism from G onto itself. The family of all automorphisms of a graph G is a permutation group.
strongly regular graph  automorphism group  orbit matrix  binary polyhedral group  icosahedron  dodecahedron  600cell  Electric multiple unit trains  highlevel maintenance planning  time window  0–1 programming model  particle swarm algorithm  fixed point  splitquaternion  quadratic polynomial  splitoctonion  neutrosophic set  neutrosophic rough set  pessimistic (optimistic) multigranulation neutrosophic approximation operators  complete lattice  rough set  matroid  operator  attribute reduction  graded rough sets  rough intuitionistic fuzzy sets  dominance relation  logical conjunction operation  logical disjunction operation  multigranulation  planar point set  convex polygon  disjoint holes  fuzzy logic  pseudoBCI algebra  quasimaximal element  KGunion  quasialternating BCKalgebra  quality function deployment  engineering characteristics  group decision making  2tuple  metro station  emergency routes  graph partitioning  graph clustering  invariant measures  partition comparison  finite automorphism groups  graph automorphisms  Fuzzy sets  ring  normed space  fuzzy normed ring  fuzzy normed ideal  fuzzy implication  quantum Balgebra  qfilter  quotient algebra  basic implication algebra  Detour–Harary index  maximum  unicyclic  bicyclic  cacti  threeway decisions  intuitionistic fuzzy sets  multigranulation rough intuitionistic fuzzy sets  granularity importance degree  complexity  Chebyshev polynomials  gear graph  pyramid graphs  edge detection  Laplacian operation  regularization  parameter selection  performance evaluation  aggregation operator  triangular norm  ?convex set  atombond connectivity index  geometric arithmetic index  line graph  generalized bridge molecular graph  graceful labeling  edge graceful labeling  edge even graceful labeling  polar grid graph  graph  good drawing  crossing number  join product  cyclic permutation  nonlinear  synchronized  linear discrete  chaotic system  algorithm  generalized permanental polynomial  coefficient  copermanental  isoperimetric number  random graph  intersection graph  social network  Abel–Grassmann’s groupoid (AGgroupoid)  Abel–Grassmann’s group (AGgroup)  involution AGgroup  commutative group  filter  graceful labeling  edge even graceful labeling  cylinder grid graph  selective maintenance  multistate system  human reliability  optimization  genetic algorithm  hypernearring  multitransformation  embedding  distance matrix (spectrum)  distance signlees Laplacian matrix (spectrum)  (generalized) distance matrix  spectral radius  transmission regular graph  graph  good drawing  crossing number  join product  cyclic permutation  cyclic associative groupoid (CAgroupoid)  cancellative  variant CAgroupoids  decomposition theorem  construction methods
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