Decision Support System Techniques Research Articles

Application of decision support system/remote sensing/GIS techniques in groundwater recharge assessment

ABSTRACT Managing water resources and storing water through the identification of groundwater recharge zones (GWRZs) are critical to water security in Egypt. Decision-support systems (DSSs), remote sensing, and GIS techniques have yielded significant data for water resources modeling. The geologic, geomorphic, climatic, and hydrologic features of the Toshka area, Western Lake Nasser, Egypt, have been generated by data from Shuttle Radar Topography Mission, Climatic Research Unit gridded Time Series, Sentinel 2 time series, and Landsat-8 OLI. Fifteen GIS thematic maps have been ranked and normalized using five DSS techniques: Analytical Hierarchy Process (AHP), Fuzzy Analytical Hierarchy process (FAHP), Frequency Ratio (FR), Shannon Entropy (SE), and Multi-Influencing Factor (MIF). To ensure the computational usefulness of these models, GWRZs have been extracted and compared. The outcomes showed that 83, 87.5, 99.1, 99.1, and 87.5% of the existing wells are in high to extreme GWRZs for AHP, FAHP, FR, SE, and MIF, respectively. The receiver operating characteristic curve (ROC) was used to assess the effectiveness of these models. It was found that the SE model had the highest predictive performance rates, as its ROC accuracy value was 91.1%, while the FR, AHP, FAHP, and MIF approaches had values of 91, 84.4, 81.9, and 89.9%, respectively.

Designing a Website-Based Analytical Hierarchy Process (AHP) in Decision Support Systems (DSS) for Parental Control of Children's Media

The massive amount of children's television programming poses a challenge for parents seeking to mediate suitable viewing habits. This research puts forward a website-based Decision Support System (DSS) leveraging Analytic Hierarchy Process to recommend appropriate shows per personalized criteria. The DSS allows parents to set priorities across factors like educational value, empathy, and social norms reflected in shows. Quantitative AHP methodology ranks options by synthesizing comparative judgments. A prototype is implemented on PHP/MySQL with an intuitive interface explaining decision logic. Testing with parent focus groups indicates significantly increased perceived utility over traditional controls. Recommendation accuracy metrics averaged 0.81 against test cases. Outcomes demonstrate feasibility and benefits of applying multi-criteria DSS techniques to aid value-based media selection for impressionable minds. The system aims to aid busy caregivers overwhelmed by the exponential growth in media choices, promoting discerning consumption. Further enhancements incorporating explanatory facilities and predictive analytics promises wider applicability inside content distribution ecosystems. This research highlights the importance of DSS-AHP integration for child development in the modern information age.

Decision support system to find a skid trail network for extracting marked trees

Careful planning of skid trails as wood extraction routes in forests is an ongoing task and necessary for minimizing the environmental damage. The main objective of this study is to find skid trails to extract marked trees from stand sites to landing sites using a GIS-based decision support system (DSS) and network analysis techniques, where environmental information is incorporated into the objective. The techniques were applied in a stand where single trees are felled in near-to-nature conditions. This system is called a single tree selection cutting method. Parameters considered for mapping the terrain stability include slope, soil and stream network. The analytical hierarchy process was used to estimate the weights of these parameters. Routing between marked trees and log landing was done using a GPS-tracking survey and then the analysis was done using new route and service area tools in GIS. The service area tool shows accessibility of marked trees by skid trails. Results showed that on average the length of the route decreased by 6.65 and 19.22% with the use of a new route tool in compartments 29 and 4, respectively. In conclusion, DSS techniques increased accessibility of marked trees and decreased the length of the route.

A Cognitively-Based Functional Taxonomy of Decision Support Techniques

The Decision Support Systems (DSS) field has grown rapidly drawing technology from many disciplines and pursuing applications in a variety of domains but developing little underlying theoretical structure, and poor linkage between research and practice. This paper presents a classification scheme for DSS techniques that provides a common theoretical framework for DSS research and structures and simplifies the process of designing application systems. The classification system is functional, grouping DSS techniques according to their ability to provide similar kinds of support (i.e. functions) to a human decision maker. It is also cognitively based, defining the kinds of support that decision maker's need in terms of architectural features and procedural aspects of human cognition. The classification is expressed as a taxonomy, encompassing six primary classes of decision support techniques representing the six general kinds of cognitive support that human decision makers need. The six classes are: process models which assist in projecting the future course of complex processes; choice models, which support integration of decision criteria across and/or alternatives; information control techniques which help in storage, retrieval, organization and integration of data and knowledge; analysis and reasoning techniques which support application of problem-specific expert reasoning procedures; representation aids which assist in expression and manipulation of a specific representation of a decision problem; and judgement amplification/refinement techniques, which help in quantification and de-biasing of heuristic judgements. Additional distinctioms are provided to distinguish the individual techniques in each of these primary categories. The taxonomy also has practical use as a design aid for decision support systems. The kinds of decision support needs represented by the taxonomy are general and can be used to guide the analysis and decomposition of a given decision prior to decision aid design. Specific needs for assistance can then be tied to specific computational techniques in the taxonomy. Methodological suggestions for using the taxonomy as a design aid are given.

A Cognitively Based Functional Taxonomy of Decision Support Techniques

The Decision Support Systems (DSS) field has grown rapidly, drawing technology from many disciplines and pursuing applications in a variety of domains but developing little underlying theoretical structure and poor linkage between research and practice. This article presents a classification scheme for DSS techniques that provides a common theoretical framework for DSS research and also structures and simplifies the process of designing application systems. The classification system is functional, grouping DSS techniques according to their ability to provide similar kinds of support (i.e., functions) to a human decision maker. It is also cognitively based, defining the kinds of support that decision makers need in terms of architectural features and procedural aspects of human cognition. The classification is expressed as a taxonomy, encompassing six primary classes of decision support techniques representing the six general kinds of cognitive support that human decision makers need. The six classes are process models, which assist in projecting the future course of complex processes; choice models, which support integration of decision criteria across aspects and/or alternatives; information control techniques, which help in storage, retrieval, organization, and integration of data and knowledge; analysis and reasoning techniques, which support application of problem-specific expert reasoning procedures; representation aids, which assist in expression and manipulation of a specific representation of a decision problem; and judgment amplification/refinement techniques, which help in quantification and debiasing of heuristic judgments. Additional distinctions are provided to distinguish the individual techniques in each of these primary categories. The taxonomy also has practical use as a design aid for decision support systems. The kinds of decision support needs represented by the taxonomy are general and can be used to guide the analysis and decomposition of a given decision prior to decision aid design. Specific needs for assistance can then be tied to specific computational techniques in the taxonomy. Methodological suggestions for using the taxonomy as a design aid are given.