Basic Data and Decision Support for Landslide Management: A Conceptual Framework

2012 ◽  
pp. 377-400
Author(s):  
Walter Pflügner
2021 ◽  
Vol 300 ◽  
pp. 113608
Author(s):  
Seda Sucu ◽  
Maria O. van Schaik ◽  
Ramazan Esmeli ◽  
Djamila Ouelhadj ◽  
Timothy Holloway ◽  
...  

Author(s):  
Mukhtar Hashemi ◽  
Enda O’Connell

Since the advent of modern computing platforms in the 1960s and despite scepticisms and uncertainties, modelling systems have become indispensable tools in water resources management. They have been postulated to support the decision-making process and hence the term decision support systems (DSSs) emerged. Hydroinformatics is a recent term compared to computational hydraulics and hydrological watershed modelling but it is an encompassing cross disciplinary concept covering hydraulics, hydrology, environmental engineering, socioeconomic and political (institutional) disciplines and it uses information and communication technologies to provide evidences for decision-makers. The aims of this chapter are two fold: (a) to review the current trends in modelling activities based on historical precedence; and (b) to present a conceptual framework for development of a comprehensive DSS using a case study approach. Hence, this chapter consists of three main parts: (1) a historical account of the DSSs, starting from early single process models to current integrated comprehensive basin-wide DSSs; (2) having established a historical perspective, case studies from Iranian experience are presented to outline a methodological (conceptual) framework for developing a comprehensive DSS. Examples of policy-relevant DSSs from the latest research are also presented. It is concluded that there would be a greater demand in the future to develop integrated policy-relevant DSSs comprising not only the technical and engineering aspects but to include the socioeconomic and political sciences as well. The new DSSs should be able to deal with uncertainties such as climate change (i.e. to have scenario analysis capabilities), be able to compare different management strategies using multi-criteria analysis tools and to include socio-economic, institutional and environmental sustainability criteria.


Author(s):  
Shah J. Miah

The Australian farm-based businesses can be benefited from specially designed applications for cost-effective operation while maximizing profits to survive in economic and environmental crises. For decision support, existing business intelligence systems (BIS) approaches scarcely deal with specific user's provisions to adjust changing situations in decision making, without extra technical exertions. In this chapter, the authors describe a conceptual framework of tailorable BIS solution that is based on case study findings in that the highlighted requirements are relevant to address changing situations through enhancing end user's engagement. The activities of end user's engagement supported through the use of tailorable features that reinforce a shift from the traditional BIS process to a new provision where business owners can actively involve in adjusting their features to their decision support.


2012 ◽  
Vol 1 (1) ◽  
pp. 90-97
Author(s):  
Naicong Li

To help synthesize and systematically organize the information, knowledge and resources for spatial decision support (SDS), and to help researchers and practitioners engaged in an actual planning process find relevant information and resources for solving their specific planning problems, the SDS Consortium and University of Redlands have developed a conceptual framework for SDS and a collection of SDS resources, hosted on the SDS Knowledge Portal. The conceptual framework includes a set of defined, inter-connected concepts pertaining to planning and spatial decision support, such as planning and decision problem types, application domains, knowledge domains and planning process including phases and steps. This conceptual framework is further used to organize a representative set of SDS resources, such as planning process workflows, methods, tools and models, data sources, case studies, literature, and so forth. The SDS Knowledge Portal facilitates learning of SDS and accessing SDS resources, promotes semantic clarity by adopting a common vocabulary for the user community, and promotes interoperability among SDS resources by using a standard set of concepts to define and classify these resources.


2004 ◽  
Vol 24 (2) ◽  
pp. 192-206 ◽  
Author(s):  
Dena M. Bravata ◽  
Kathryn M. McDonald ◽  
Herbert Szeto ◽  
Wendy M. Smith ◽  
Chara Rydzak ◽  
...  

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