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Data for: MANY-OBJECTIVE PORTFOLIO OPTIMIZATION APPROACH FOR STORMWATER MANAGEMENT PROJECT SELECTION ENCOURAGING DECISION MAKER BUY-IN

Main Author: Di Matteo, Michael
Other Authors: Dandy, Graeme, Maier, Holger
Format: Dataset
Terbitan: Mendeley , 2018
Subjects:
Civil Engineering
Optimization (Algorithms)
Stormwater Management
Online Access: https:/data.mendeley.com/datasets/pxbhmh64vb
ctrlnum 0.17632-pxbhmh64vb.1
fullrecord <?xml version="1.0"?> <dc><creator>Di Matteo, Michael</creator><title>Data for: MANY-OBJECTIVE PORTFOLIO OPTIMIZATION APPROACH FOR STORMWATER MANAGEMENT PROJECT SELECTION ENCOURAGING DECISION MAKER BUY-IN</title><publisher>Mendeley</publisher><description>Additional Supporting Information (Files uploaded separately) &#x2022; Data Set S1. An excel spreadsheet containing stormwater harvesting and green score determined by stakeholders, and calculations for costings and water quality performance of individual BMPs. Filename: ds01.xlsx &#x2022; Data Set S2. A compressed file containing inputs and an executable for the Pareto Ant Colony Optimization Algorithm (PACOA) that can be run on a Windows desktop computer to replicate the optimization results. Filename: ds02.rar &#x2022; Data Set S3. An excel spreadsheet containing the objective function values, decision options, and alternative data (catchment size, breakdown of benefits by Council) of the Pareto optimal solutions determined by sorting the optimization results for non-dominance in objective space. Filename: ds03.csv &#x2022; Data Set S4. A .ddv that can be opened in the DiscoveryDV visual analytics program, containing interactive visualization of the Pareto optimal solution data from Data Set S3. Filename: ds04.ddv </description><subject>Civil Engineering</subject><subject>Optimization (Algorithms)</subject><subject>Stormwater Management</subject><contributor>Dandy, Graeme</contributor><contributor>Maier, Holger</contributor><type>Other:Dataset</type><identifier>10.17632/pxbhmh64vb.1</identifier><rights>Attribution-NonCommercial 3.0 Unported</rights><rights>https://creativecommons.org/licenses/by-nc/3.0</rights><relation>https:/data.mendeley.com/datasets/pxbhmh64vb</relation><date>2018-09-25T22:54:52Z</date><recordID>0.17632-pxbhmh64vb.1</recordID></dc>
format Other:Dataset
Other
author Di Matteo, Michael
author2 Dandy, Graeme
Maier, Holger
title Data for: MANY-OBJECTIVE PORTFOLIO OPTIMIZATION APPROACH FOR STORMWATER MANAGEMENT PROJECT SELECTION ENCOURAGING DECISION MAKER BUY-IN
publisher Mendeley
publishDate 2018
topic Civil Engineering
Optimization (Algorithms)
Stormwater Management
url https:/data.mendeley.com/datasets/pxbhmh64vb
contents Additional Supporting Information (Files uploaded separately) • Data Set S1. An excel spreadsheet containing stormwater harvesting and green score determined by stakeholders, and calculations for costings and water quality performance of individual BMPs. Filename: ds01.xlsx • Data Set S2. A compressed file containing inputs and an executable for the Pareto Ant Colony Optimization Algorithm (PACOA) that can be run on a Windows desktop computer to replicate the optimization results. Filename: ds02.rar • Data Set S3. An excel spreadsheet containing the objective function values, decision options, and alternative data (catchment size, breakdown of benefits by Council) of the Pareto optimal solutions determined by sorting the optimization results for non-dominance in objective space. Filename: ds03.csv • Data Set S4. A .ddv that can be opened in the DiscoveryDV visual analytics program, containing interactive visualization of the Pareto optimal solution data from Data Set S3. Filename: ds04.ddv
id IOS7969.0.17632-pxbhmh64vb.1
institution Universitas Islam Indragiri
affiliation onesearch.perpusnas.go.id
institution_id 804
institution_type library:university
library
library Teknologi Pangan UNISI
library_id 2816
collection Artikel mulono
repository_id 7969
city INDRAGIRI HILIR
province RIAU
shared_to_ipusnas_str 1
repoId IOS7969
first_indexed 2020-04-08T08:31:26Z
last_indexed 2020-04-08T08:31:26Z
recordtype dc
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score 17.538404

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