scholarly journals A Decision Support System for Plant Optimization in Urban Areas with Diversified Solar Radiation

2017 ◽  
Vol 9 (2) ◽  
pp. 215 ◽  
Author(s):  
Heyi Wei ◽  
Zhengdong Huang ◽  
Mu Lin
Keyword(s):  
Decision Support ◽  
Solar Radiation ◽  
Decision Support System ◽  
Support System ◽  
Urban Areas ◽  
Plant Optimization

scholarly journals Decision support system for urban flood management

2005 ◽  
Vol 7 (1) ◽  
pp. 3-15 ◽  
Author(s):  
A. J. Abebe ◽  
R. K. Price
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Acquisition ◽  
Support System ◽  
Urban Areas ◽  
Flood Management ◽  
Urban Flood ◽  
Flood Warning ◽  
Forecast Models

This paper presents the development of a decision support system (DSS) for flood warning and instantiation of restoration activities in two urban areas, the Liguria Region in Italy and the Greater Athens catchment in Greece, with the potential of extension to other locations with similar flooding problems. The tool is designed to work at the centre of a set of meteorological and hydrologic/hydraulic forecast models together with telemetric data acquisition networks. The study reveals the complexity and uncertainty involved in managing flooding in the study areas. Issues about the validity and extended benefits of the system are also discussed.

scholarly journals An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

2020 ◽  
Vol 12 (2) ◽  
pp. 259 ◽  
Author(s):  
Małgorzata Sztubecka ◽  
Marta Skiba ◽  
Maria Mrówczyńska ◽  
Anna Bazan-Krzywoszańska
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Decision Support ◽  
Geographic Information Systems ◽  
Decision Support System ◽  
Support System ◽  
Urban Areas ◽  
Renewable Energy Sources ◽  
Decision Makers ◽  
Energy Sources

Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.

Clinical Decision Support System in Virtual Clinic

2018 ◽  
Author(s):  
Atta Rahman
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Information And Communication Technologies ◽  
Support System ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Research Work ◽  
Healthcare Services ◽  
Remote Areas ◽  
Virtual Clinic

Healthcare facilities in Pakistan are not sufficient, particularly in rural or remote areas. These areas show poorer health outcomes than urban areas due to lack of trained physicians, clinics and healthcare services. This is mainly because, most of the healthcare practitioners don’t like to practice in these areas. In contrast, a huge number of lady doctors don’t practice after their graduation due to various reasons, thus major portion of medical human resource is not utilized. Moreover, the information and communication technologies (ICT) in Pakistan are becoming popular and affordable day by day. Many developing countries like Pakistan have started the implementation of telemedicine projects. This research work focuses on connecting the remote areas of Pakistan to the urban areas’ practices and doctors especially the non-practicing lady doctors through ICT by establishing virtual clinics in remote areas. In addition to that, the virtual clinic decision support system (VCDSS) facilitates the users in two ways. Firstly, automatically assigning the appropriate most doctor for patient based on his ranking, experience and specialization etc. using a fuzzy rule based system (FRBS). Secondly, it helps the registered doctors at the time of prescribing a patient by suggesting the medicines according to the patient’s symptoms and history. It is done by using the knowledge-base of past diagnoses/prescriptions by the same doctor as well as other doctors and British National Formulary (BNF) using Apriori Algorithm and Inductive Learning Algorithm. The results are shown using sample dataset.

A Decision Support System for On-Demand Goods Delivery Using Shared Autonomous Electric Vehicles

2019 ◽  
Vol 11 (2) ◽  
pp. 72-88
Author(s):  
Amel Jaoua ◽  
Marouen Ben Ammar ◽  
Anjali Awasthi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Electric Vehicles ◽  
Support System ◽  
Urban Areas ◽  
Strategic Decision ◽  
Fleet Size ◽  
Fleet Sizing ◽  
On Demand ◽  
Autonomous Electric Vehicles

This article presents a strategic decision support system (DSS) for on-demand delivery companies in urban areas. This DSS is designed and developed for the promising new concept of goods delivery based on a fleet of Shared Autonomous Electric Vehicles (SAEVs). A simulation-based optimization model is proposed to solve the fleet sizing and composition problems. The efficiency of the developed strategic DSS in determining best fleet size and composition under different scenarios is demonstrated. This article provides managerial insights to help goods delivery companies, who intend to use SAEVs, in determining the type and number of vehicles to acquire.

Development of a Decision Support System for Flood Warning in Jinan City

2011 ◽  
Vol 219-220 ◽  
pp. 1267-1270 ◽  
Author(s):  
Chuan Qi Li ◽  
Chao Jia ◽  
Bang Shu Xu
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Urban Areas ◽  
Flood Simulation ◽  
Urban Flood ◽  
Web Based ◽  
Flood Warning ◽  
Jinan City

A decision support system for flood warning has been developed for Jinan city. It is a web based distributed system that integrates GIS, databases and models. Urban Flood Simulation model is used as a real-time flood forecasting model. Mike Flood model is used to simulate pre-formulated flood scenarios for urban areas. The objective of the system is to simulate and forecast river and urban floods on the basis of real-time meteorological situation and rainfall available, and to serve as a tool for making decision.

scholarly journals A literature review: model of disaster risk reduction for decision support system

2021 ◽  
Vol 331 ◽  
pp. 04011
Author(s):  
Soetjipto Jojok Widodo ◽  
Hidayah Entin ◽  
Sholikhah Faizatus
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Risk Reduction ◽  
Support System ◽  
Urban Areas ◽  
Disaster Risk Reduction ◽  
Human Life ◽  
Disaster Risk ◽  
Review Model ◽  
The Right

Disaster is a threat to human life. Many losses are caused by disasters, namely loss of life, injured people, loss of homes, and others. In addition, the frequency and intensity of disasters are also increasing every year. Therefore, research on Disaster Risk Reduction (DRR) is needed both to reduce disaster risk and to manage the disaster. The purpose of this research is to develop an appropriate DRR model in an area to assist decision-making in making policy. This research was compiled based on literature studies from various reputable journals to be used as a reference in the preparation of the right model. Then proceed with the development of a framework to model an efficient and effective DRR. The steps for making a holistic DRR model have been identified and the test design for the model has been determined, namely simulation, validation, and scenario. The recommendation given from this study is the preparation of a DSS (Decision Support System) as a tool for decision-makers to make policies regarding DRR-based regional development. This discussion will be continued in the next research by including case studies in certain urban areas.

scholarly journals AI-Based Decision System to Evaluate Scenarios of Response to the COVID-19 Pandemic. Evidence From Barcelona and Manhattan

2021 ◽  
Author(s):  
Didier Grimaldi ◽  
Hugo Arboleda ◽  
Karla Esqui
Keyword(s):  
New York ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Urban Areas ◽  
Large Scale ◽  
Social Consequences ◽  
Local Economy ◽  
Effective Response ◽  
Medium Businesses

Abstract In 2020, the COVID-19 outbreak has had severe economic and social consequences all over the planet. Traditional national health systems have been unable to predict and to provide a decision support system able to coordinate an effective response to the outbreak and to stop the rapid spread of this disease. In this current manuscript, we have decided to focus our analysis on Small and Medium Businesses (SMBs). While some SMBs have survived, many others, particularly in urban areas, have had to shut down as a direct decision of government restrictions. Our study presents a decision support system based on Artificial Intelligence which helps governments to prioritize the closures of SMBs located in a city. Indeed, the decision to shut down may vary according to the relative danger that the business premises represent as a social gathering point and its benefits for the local economy. In this vein, we analyse 3 different scenarios which assume different financial and social costs. The visualization of the results on a city map provides additional value for the decision-making process. The Urban Decision Support System is tested by two case studies: Barcelona and New York City. This research has implications for practitioners to support their decision to close-down the economies in the event of another large-scale outbreak. It has also research implications as new evidence that data analytics could be an additional and valuable source of information for decision support processes.

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