Environmental decision support systems: A new approach to support the operation and maintenance of horizontal subsurface flow constructed wetlands

Turon, Clàudia; Comas, Joaquím; Alemany, Jaume; Cortés, Ulises; Poch, M.

doi:10.1016/j.ecoleng.2007.04.012

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…In quality there are very few application areas including software quality improvement ) and classifying lake water chemistry (Saunders et al 2005). Applications of KBS in maintenance include developing intelligent product manuals (Setchi et al 2006), the development of an environmental DSS to improve the operation and maintenance of constructed wetlands (Turon et al 2007) and a KBS for maintenance planning of highway concrete bridges (Chassiakos et al 2005). In fault diagnosis, KBSs are used in implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process plants (Qian et al 2005), fault diagnosis of a chemical process operation, real time fault diagnosis of a lubricating oil refining process (Qian et al 2008), a Web-based hydro turbine fault diagnosis system (Song et al 2008) and in motorcycle maintenance (Su et al 2006).…”

Section: Kbs In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Kbs In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Others describe optimizing water allocation [45], case-based reasoning systems and rule-based systems for wastewater management of conventional systems [6,8,10,11,[46][47][48][49][50], and natural systems [12,51] or fuzzy systems and genetic algorithms for modeling rainfall [52] and calibrating a rainfall-runoff model [53]. Different AI techniques such as neural networks, genetic algorithms, and intelligent control are also used recently for the modeling and management of rain water reservoirs in real time [54].…”

Section: Existing or Proposed Edss For Iwrmmentioning

confidence: 99%

“…Once implemented an EDSS (like any knowledge-based system), it has to be evaluated for what it knows, for how it uses what it knows, for how fast it can learn something new, and finally, for its overall performance. Figure 1 shows schematically the methodology proposed by our research group, which has been successfully applied to different water management problems (see, e.g., [6][7][8][9][10][11][12]). …”

Section: A Procedures For Edss Developmentmentioning

confidence: 99%

Decision Support Systems for Integrated Water Resources Management Under Water Scarcity

Comas

Poch

2009

The Handbook of Environmental Chemistry

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Environmental decision support systems (EDSS) can be defined as intelligent information systems that are able to integrate numerical models (linear or nonlinear) with artificial intelligence (AI) techniques, together with geographical information and environmental ontologies. EDSS emulate the expert's reasoning processes in decision making, integrating geographical information and environmental ontologies, reducing the time in which decisions are made, as well as improving the consistency and quality of those decisions. EDSS can be useful to cope with the complexity and multidisciplinary nature of Integrated Water Resource Management (IWRM). Besides, the Drought Management Plan Report written by the Water Scarcity and Droughts Expert Network (November 2007) states the "Development of decision support systems for the best exploitation of all information available, including drought forecasts, in order to optimize drought management and mitigation measures" as one of the main needs for advances in drought research. The necessity to confront complexity when dealing with IWRM is first stated in the paper. Then an EDSS as a promising tool to cope with this complexity is introduced, putting special emphasis in a proposal for EDSS development. Next the paper reviews the more relevant decision support systems (DSS) published for IWRM under water scarcity. Classical DSS to model environmental systems as well as those DSS including knowledge-based or AI techniques (i.e., EDSS) are reviewed. Open questions related to the EDSS development and application include improvement of data and knowledge acquisition and knowledge implementation methods, better integration of numerical models and AI techniques (both in simulation and real applications), evaluation of EDSS (lack of benchmarks), protocols to facilitate sharing and reuse of knowledge, involvement of end-users, and probably others.

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“…The final decision is based on a weighted sum model based on results from six independent assessments. Turon et al (2007) …”

Section: Sustainable Water and Wastewater Utilitiesmentioning

confidence: 99%

Sustainability

Zhou¹,

Montalto²,

Erdal³

et al. 2009

Water Environment Research

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This review section covers journal articles and conferences papers on sustainability that were published from 2006 to 2008. The literature review has been divided into the following sections 1 : • Sustainable water and wastewater utilities The sustainable water and wastewater utilities reviewed sustainable practices and sustainable workforce management including workforce development, knowledge retention and information sharing, succession planning, and financing. The stormwater and green Infrastructure considered a variety of low impact development (LID) design and technologies and green infrastructures. The wastewater technologies reviewed wastewater treatment, anaerobic technologies, low impact and natural treatment systems, and residuals and emissions management. The sustainability and energy in wastewater industry discussed energy conservation, production and recovery. The sustainability and climate change reviewed greenhouse gas (GHG) emission from water and wastewater industries, the impacts of climate change and reducing carbon footprint.The sustainability and water reuse considered water reclamation and reuse in municipal, industrial, and agricultural settings

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Environmental decision support systems: A new approach to support the operation and maintenance of horizontal subsurface flow constructed wetlands

Cited by 19 publications

References 26 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Decision Support Systems for Integrated Water Resources Management Under Water Scarcity

Sustainability

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