000 04139nam a22005295i 4500
001 978-94-007-7506-0
003 DE-He213
005 20140220082531.0
007 cr nn 008mamaa
008 131125s2014 ne | s |||| 0|eng d
020 _a9789400775060
_9978-94-007-7506-0
024 7 _a10.1007/978-94-007-7506-0
_2doi
050 4 _aGB1001-1199.8
072 7 _aRBK
_2bicssc
072 7 _aSCI081000
_2bisacsh
082 0 4 _a551.4
_223
100 1 _aAraghinejad, Shahab.
_eauthor.
245 1 0 _aData-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering
_h[electronic resource] /
_cby Shahab Araghinejad.
264 1 _aDordrecht :
_bSpringer Netherlands :
_bImprint: Springer,
_c2014.
300 _aXIII, 292 p. 142 illus., 79 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aWater Science and Technology Library,
_x0921-092X ;
_v67
505 0 _aPreface -- 1. Introduction -- 2. Basic Statistics -- 3. Regression Based Models -- 4. Time Series Modeling -- 5. Artificial Neural Networks -- 6. Support Vector Machines.- 7. Fuzzy Models -- 8. Hybrid Models and Multi Model Data Fusion -- Appendix -- Index.
520 _a“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques.    The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.
650 0 _aGeography.
650 0 _aHydraulic engineering.
650 0 _aEnvironmental management.
650 0 _aEnvironmental sciences.
650 0 _aEnvironmental pollution.
650 1 4 _aEarth Sciences.
650 2 4 _aHydrogeology.
650 2 4 _aHydrology/Water Resources.
650 2 4 _aMath. Appl. in Environmental Science.
650 2 4 _aWaste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution.
650 2 4 _aEnvironmental Monitoring/Analysis.
650 2 4 _aEnvironmental Management.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9789400775053
830 0 _aWater Science and Technology Library,
_x0921-092X ;
_v67
856 4 0 _uhttp://dx.doi.org/10.1007/978-94-007-7506-0
912 _aZDB-2-EES
999 _c94050
_d94050