語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
FindBook
Google Book
Amazon
博客來
Developing a Plugin in QGIS for Selecting the Location of a New Manufacturing Plant.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Developing a Plugin in QGIS for Selecting the Location of a New Manufacturing Plant./
作者:
Ahmadzadeh Siyahrood, Sanaz.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:
175 p.
附註:
Source: Masters Abstracts International, Volume: 83-03.
Contained By:
Masters Abstracts International83-03.
標題:
Information technology. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28715433
ISBN:
9798535597171
Developing a Plugin in QGIS for Selecting the Location of a New Manufacturing Plant.
Ahmadzadeh Siyahrood, Sanaz.
Developing a Plugin in QGIS for Selecting the Location of a New Manufacturing Plant.
- Ann Arbor : ProQuest Dissertations & Theses, 2021 - 175 p.
Source: Masters Abstracts International, Volume: 83-03.
Thesis (M.S.)--The University of North Carolina at Charlotte, 2021.
This item must not be sold to any third party vendors.
With the growth of the technology-driven world, today many designs and analyzes depend on smart software to address different computational concepts. In the meantime, locating and finding a suitable place for establishing a facility is one of them and is considered by urban designers, regional planners, and architects. Accordingly, the main goal of this study is developing a plugin in QGIS to aid in the decision-making of selecting the location of a new manufacturing plant by prioritizing the places that have the most renewable energies. Considering this logic has two main purposes; the first one is renewable resources, such as sunlight, wind, rain, tides, waves, and geothermal heat can supply all the energies needed for the productions of these factories while causing as little harm to the environment as possible. Second, we can locate these factories in locations with a low unemployment rate while providing maximum suitable conditions and facilities for the workers, thus helping to reduce unemployment rates in those areas. To reach these main goals, we developed a computational system titled the site selection decision making (SSDM | Site Selection Decision Making) plugin in QGIS3.12 software. The clustering method was used for clustering the important locations based on their accessibility to other facilities. Then binary classification which is a supervised machine learning algorithm, and its goal is to predict categorical class labels including discrete and unordered format was used for analysis and returning the final results. Pycaret library; pycaret.classification has been used for implementing the machine learning algorithm. In this regard, binary classification determines whether a site is suitable for establishing a new industrial factory or not. Therefore, its answer is yes or no considering several significant factors.
ISBN: 9798535597171Subjects--Topical Terms:
532993
Information technology.
Subjects--Index Terms:
Location
Developing a Plugin in QGIS for Selecting the Location of a New Manufacturing Plant.
LDR
:03031nmm a2200385 4500
001
2352504
005
20221128104001.5
008
241004s2021 ||||||||||||||||| ||eng d
020
$a
9798535597171
035
$a
(MiAaPQ)AAI28715433
035
$a
AAI28715433
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Ahmadzadeh Siyahrood, Sanaz.
$3
3692140
245
1 0
$a
Developing a Plugin in QGIS for Selecting the Location of a New Manufacturing Plant.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2021
300
$a
175 p.
500
$a
Source: Masters Abstracts International, Volume: 83-03.
500
$a
Advisor: Ellinger, Jefferson.
502
$a
Thesis (M.S.)--The University of North Carolina at Charlotte, 2021.
506
$a
This item must not be sold to any third party vendors.
520
$a
With the growth of the technology-driven world, today many designs and analyzes depend on smart software to address different computational concepts. In the meantime, locating and finding a suitable place for establishing a facility is one of them and is considered by urban designers, regional planners, and architects. Accordingly, the main goal of this study is developing a plugin in QGIS to aid in the decision-making of selecting the location of a new manufacturing plant by prioritizing the places that have the most renewable energies. Considering this logic has two main purposes; the first one is renewable resources, such as sunlight, wind, rain, tides, waves, and geothermal heat can supply all the energies needed for the productions of these factories while causing as little harm to the environment as possible. Second, we can locate these factories in locations with a low unemployment rate while providing maximum suitable conditions and facilities for the workers, thus helping to reduce unemployment rates in those areas. To reach these main goals, we developed a computational system titled the site selection decision making (SSDM | Site Selection Decision Making) plugin in QGIS3.12 software. The clustering method was used for clustering the important locations based on their accessibility to other facilities. Then binary classification which is a supervised machine learning algorithm, and its goal is to predict categorical class labels including discrete and unordered format was used for analysis and returning the final results. Pycaret library; pycaret.classification has been used for implementing the machine learning algorithm. In this regard, binary classification determines whether a site is suitable for establishing a new industrial factory or not. Therefore, its answer is yes or no considering several significant factors.
590
$a
School code: 0694.
650
4
$a
Information technology.
$3
532993
650
4
$a
Geographic information science.
$3
3432445
650
4
$a
Manufacturing.
$3
3389707
650
4
$a
Collaboration.
$3
3556296
650
4
$a
Decision making.
$3
517204
653
$a
Location
653
$a
Machine learning
653
$a
Manufacturing plant
653
$a
Plugin
653
$a
PyCaret
653
$a
QGIS
690
$a
0729
690
$a
0489
690
$a
0370
710
2
$a
The University of North Carolina at Charlotte.
$b
Architecture.
$3
3347805
773
0
$t
Masters Abstracts International
$g
83-03.
790
$a
0694
791
$a
M.S.
792
$a
2021
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28715433
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9474942
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入