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This paper provides an overview of multi-criteria decision analysis (MCDA) applications in managing water-related disasters (WRD). Although MCDA has been widely used in managing natural disasters, it appears that no literature review has been conducted on the applications of MCDA in the disaster management phases of mitigation, preparedness, response, and recovery. Therefore, this paper fills this gap by providing a bibliometric analysis of MCDA applications in managing flood and drought events. Out of 818 articles retrieved from scientific databases, 149 articles were shortlisted and analyzed using a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) approach. The results show a significant growth in MCDA applications in the last five years, especially in managing flood events. Most articles focused on the mitigation phase of DMP, while other phases of preparedness, response, and recovery remained understudied. The analytical hierarchy process (AHP) was the most common MCDA technique used, followed by mixed-method techniques and TOPSIS. The article concludes the discussion by identifying a number of opportunities for future research in the use of MCDA for managing water-related disasters.

Reaching a decision when multiple, possibly conflicting, criteria are taken into account is often a difficult task. This normally requires the intervention of an analyst to aid the decision maker in following a clear methodology with respect to the steps that need to be taken, as well as the use of different algorithms and software tools. Most of these tools focus on one or a small number of algorithms, some are difficult to adapt and interface with other tools, while only a few belong to dynamic communities of contributors allowing them to expand in use and functionality. In this paper, we address these issues by proposing to use the R statistical environment and the MCDA package of decision aiding algorithms and tools. This package is meant to provide a wide range of MCDA algorithms that may be used by an analyst to tailor a decision aiding process to their needs, while the choice of R takes advantage of the yet poorly explored opportunity to interface data analysis and decision aiding. We additionally demonstrate the use of this tool on a practical application following a well-defined decision aiding process.

Measuring real-world impact is vital for demonstrating the success of a project and one of the most direct ways to justify taxpayers’ contributions towards public funding. Impact reporting should identify and examine the potential positive and negative consequences of the continuing operations of a proposed project and suggest strategies to expand, further develop, mitigate, avoid or offset them. Designing a tool or methodology that will capture the impact of collaborative research and innovation projects related to sustainability requires input from technical experts but also from experts in the domains of survey design and communication. Without survey design insights and testing it can be very difficult to achieve unambiguous and accurate reporting of impacts. This paper proposes six key recommendations that should be considered for those monitoring projects when identifying metrics and designing a sustainability impact report. These recommendations stem from a series of in-depth interviews about sustainability and innovation impact reporting with research project co-ordinators in the process industries (e.g., cement, ceramics, chemicals, engineering, minerals and ores, non-ferrous metals, steel and water sectors). Our results show that factors such as ambiguous terminology, two-in-one questions, the stage of the project, over-hypothetical estimates, inadequate formats and alternatives and lack of guidelines can negatively influence the data collected in usual project monitoring activities and jeopardise the overall validity of the reporting. This work acts as a guideline for those monitoring to improve how they ask for impact data from projects, whether they are introducing new impact metrics or evaluating existing ones.