語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Hydrological Data Driven Modelling =...
~
Mathew, Jimson.
Hydrological Data Driven Modelling = A Case Study Approach /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Hydrological Data Driven Modelling/ by Renji Remesan, Jimson Mathew.
其他題名:
A Case Study Approach /
作者:
Remesan, Renji.
其他作者:
Mathew, Jimson.
面頁冊數:
XV, 250 p. 172 illus., 59 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Hydrogeology. -
電子資源:
https://doi.org/10.1007/978-3-319-09235-5
ISBN:
9783319092355
Hydrological Data Driven Modelling = A Case Study Approach /
Remesan, Renji.
Hydrological Data Driven Modelling
A Case Study Approach /[electronic resource] :by Renji Remesan, Jimson Mathew. - 1st ed. 2015. - XV, 250 p. 172 illus., 59 illus. in color.online resource. - Earth Systems Data and Models,12364-5830 ;. - Earth Systems Data and Models,1.
Introduction -- Hydroinformatics and Data based Modelling Issues in Hydrology -- Hydroinformatics and Data based Modelling Issues in Hydrology -- Model Data Selection and Data Pre-processing Approaches -- Machine Learning and Artificial Intelligence Based Approaches -- Data based Solar Radiation Modelling -- Data based Rainfall-Runoff Modelling -- Data based Evapotranspiration Modelling -- Application of Statistical Blockade in Hydrology.
This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
ISBN: 9783319092355
Standard No.: 10.1007/978-3-319-09235-5doiSubjects--Topical Terms:
670389
Hydrogeology.
LC Class. No.: GB1001-1199.8
Dewey Class. No.: 551.4
Hydrological Data Driven Modelling = A Case Study Approach /
LDR
:02734nam a22004095i 4500
001
965283
003
DE-He213
005
20200630053418.0
007
cr nn 008mamaa
008
201211s2015 gw | s |||| 0|eng d
020
$a
9783319092355
$9
978-3-319-09235-5
024
7
$a
10.1007/978-3-319-09235-5
$2
doi
035
$a
978-3-319-09235-5
050
4
$a
GB1001-1199.8
072
7
$a
RBK
$2
bicssc
072
7
$a
SCI081000
$2
bisacsh
072
7
$a
RBK
$2
thema
082
0 4
$a
551.4
$2
23
100
1
$a
Remesan, Renji.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1063990
245
1 0
$a
Hydrological Data Driven Modelling
$h
[electronic resource] :
$b
A Case Study Approach /
$c
by Renji Remesan, Jimson Mathew.
250
$a
1st ed. 2015.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2015.
300
$a
XV, 250 p. 172 illus., 59 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Earth Systems Data and Models,
$x
2364-5830 ;
$v
1
505
0
$a
Introduction -- Hydroinformatics and Data based Modelling Issues in Hydrology -- Hydroinformatics and Data based Modelling Issues in Hydrology -- Model Data Selection and Data Pre-processing Approaches -- Machine Learning and Artificial Intelligence Based Approaches -- Data based Solar Radiation Modelling -- Data based Rainfall-Runoff Modelling -- Data based Evapotranspiration Modelling -- Application of Statistical Blockade in Hydrology.
520
$a
This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
650
0
$a
Hydrogeology.
$3
670389
650
0
$a
Hydrology.
$3
642066
650
0
$a
Engineering geology.
$3
558458
650
0
$a
Engineering—Geology.
$3
1253941
650
0
$a
Foundations.
$3
565339
650
0
$a
Hydraulics.
$3
636781
650
2 4
$a
Hydrology/Water Resources.
$3
1020964
650
2 4
$a
Geoengineering, Foundations, Hydraulics.
$3
768429
700
1
$a
Mathew, Jimson.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
883995
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319092362
776
0 8
$i
Printed edition:
$z
9783319092348
776
0 8
$i
Printed edition:
$z
9783319350288
830
0
$a
Earth Systems Data and Models,
$x
2364-5830 ;
$v
1
$3
1260865
856
4 0
$u
https://doi.org/10.1007/978-3-319-09235-5
912
$a
ZDB-2-EES
912
$a
ZDB-2-SXEE
950
$a
Earth and Environmental Science (SpringerNature-11646)
950
$a
Earth and Environmental Science (R0) (SpringerNature-43711)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入