scholarly journals A Spatial Multi-criteria Decision Support System for Stress Recovery-Oriented Forest Management

2021 ◽  
pp. 171-184
Author(s):  
Irene Capecchi ◽  
Gianluca Grilli ◽  
Elena Barbierato ◽  
Sandro Sacchelli
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support System ◽  
Support System ◽  
Forest Type ◽  
Well Being ◽  
Research Area ◽  
Stress Recovery ◽  
Recovery Potential ◽  
Urban Settlements

AbstractA solution to cope with chaotic urban settlements and frenetic everyday life is refuging in nature as a way to reduce stress. In general—in recent years—it has been scientifically demonstrated how natural areas are an important environment for psycho-physiological health. As a consequence, it is important to plan dedicated spaces for stress recovery in order to increase the well-being of people. With respect to forests, there is a growing interest in understanding the marketing and tourist potential of forest-therapy activities and policies. This paper develops a decision support system (DSS) for decision makers, based on geographic information system to define the suitability of forest areas to improve psychological and physiological human well-being. Innovative technologies such as electroencephalography (EEG) and virtual reality (VR) are applied to test human status. The DSS combines four sets of indicators in a multi-attribute decision analysis and identifies the areas with the largest stress-recovery potential. Two multi-attribute model—one in summer and one in winter—are elaborated to obtain a dynamic evaluation of suitability. Results show significant differences among forest type, forest management, altitude range, and season in terms of stand suitability. EEG and VR seem to be promising technologies in this research area. Strengths and weaknesses of the approach, as well as potential future improvement and implications for territorial marketing, are suggested.

A GIS-BASED DECISION SUPPORT SYSTEM FOR FOREST MANAGEMENT PLANS IN TURKEY

2010 ◽  
Vol 9 (7) ◽  
pp. 929-937 ◽  
Author(s):  
Fatih Sivrikaya ◽  
Emin Zeki Baskent ◽  
Ugur Sevik ◽  
Caner Akgul ◽  
Ali Ihsan Kadiogullari ◽  
...  
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support System ◽  
Support System ◽  
Management Plans

scholarly journals Assisting Sustainable Forest Management and Forest Policy Planning with the Sim4Tree Decision Support System

Forests ◽  
2015 ◽  
Vol 6 (12) ◽  
pp. 859-878 ◽  
Author(s):  
Floris Dalemans ◽  
Paul Jacxsens ◽  
Jos Van Orshoven ◽  
Vincent Kint ◽  
Pieter Moonen ◽  
...  
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support System ◽  
Support System ◽  
Sustainable Forest Management ◽  
Forest Policy ◽  
Policy Planning

Methodologies of Legacy Clinical Decision Support System

2017 ◽  
Vol 2 (2) ◽  
pp. 20-37
Author(s):  
Meenakshi Sharmi ◽  
Himanshu Aggarwal
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Area

Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.

scholarly journals Initiative For Thyroid Cancer Diagnosis: Decision Support System For Anaplast Thyroid Cancer

2017 ◽  
Vol 1 (2) ◽  
pp. 48
Author(s):  
Jamil Ahmed Chandio ◽  
M. Abdul Rehman Soomrani ◽  
Attaullah Sehito ◽  
Shafaq Siddiqui
Keyword(s):  
Thyroid Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Confusion Matrix ◽  
Measurement Techniques ◽  
Research Area ◽  
Classification Problems ◽  
Biomedical Image ◽  
High Level

Due to the high level exposure of biomedical image analysis, Medical image mining has become one of the well-established research area(s) of machine learning. AI (Artificial Intelligence) techniques have been vastly used to solve the complex classification problems of thyroid cancer. Since the persistence of copycat chromatin properties and unavailability of nuclei measurement techniques, it is really problem for doctors to determine the initial phases of nuclei enlargement and to assess the early changes of chromatin distribution. For example involvement of multiple transparent overlapping of nuclei may become the cause of confusion to infer the growth pattern of nuclei variations. Un-decidable nuclei eccentric properties may become one of the leading causes for misdiagnosis in Anaplast cancers. In-order to mitigate all above stated problems this paper proposes a novel methodology so called “Decision Support System for Anaplast Thyroid Cancer” and it proposes a medical data preparation algorithm AD (Analpast_Cancers) which helps to select the appropriate features of Anaplast cancers such as (1) enlargement of nuclei, (2) persistence of irregularity in nuclei and existence of hyper chromatin. Proposed methodology comprises over four major layers, first layer deals with the noise reduction, detection of nuclei edges and object clusters. Second layer selects the features of object of interest such as nuclei enlargement, irregularity and hyper chromatin. Third layer constructs the decision model to extract the hidden patterns of disease associated variables and final layer evaluates the performance evaluation by using confusion matrix, precision and recall measures. The overall classification accuracy is measured about 97.2% with 10-k fold cross validation.

scholarly journals Web-Based Forest Resources Management Decision Support System

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1079 ◽  
Author(s):  
Marco Marto ◽  
Keith Reynolds ◽  
José Borges ◽  
Vladimir Bushenkov ◽  
Susete Marques ◽  
...  
Keyword(s):  
Decision Support ◽  
Ecosystem Services ◽  
Forest Management ◽  
Decision Support System ◽  
Support System ◽  
Remote Access ◽  
Web Based ◽  
Management Planning ◽  
Forest Management Planning ◽  
The Web

In this paper, we present a web-based decision support system (DSS)—wSADfLOR—to facilitate the access of stakeholders to tools that may contribute to enhancing forest management planning. The emphasis is on a web-based architecture and a web graphic user interface (wGUI) that may effectively support the analysis of trade-offs between ecosystem services in order to address participatory and sustainable forest management objectives. For that purpose, the wGUI provides remote access to a management information system, enabling users to analyze environmental and biometric data and topological information as well. Moreover, the wGUI provides remote access to forest simulators so that users may define and simulate prescriptions such as chronological sequences of management options and the corresponding forest ecosystem services outcomes. Remote access to management planning methods is further provided so that users may input their objectives and constraints. The wGUI delivers information about tradeoffs between ecosystem services in the form of decision maps so that users in different locations may negotiate bundles of ecosystem services as well as the plan needed to provide them. The multiple criteria programming routines provide proposals for management plans that may be assessed further, using geographical and alphanumeric information provided by the wGUI. Results for an application to a forested landscape extending to 14,388 ha are presented and discussed. This landscape provides several ecosystem services and the development of its management plan involves multiple stakeholders. Results show that the web-based architecture and the wGUI provide effective access for stakeholders to information about the forest management planning area and to decision support tools that may contribute to addressing complex multi-objective and multiple-decision-maker management planning contexts. They also highlight that the involvement and participation of stakeholders in the design of the web-based architecture contributes to assuring the quality and the usability of the system.

scholarly journals Forest management strategies to reduce spruce budworm damage in the Fundy Model Forest

1996 ◽  
Vol 72 (4) ◽  
pp. 399-405 ◽  
Author(s):  
David A. MacLean
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support System ◽  
Support System ◽  
Tree Mortality ◽  
Choristoneura Fumiferana ◽  
Management Strategies ◽  
Spruce Budworm ◽  
Stand Age ◽  
Timber Supply

Spruce budworm (Choristoneura fumiferana) outbreaks cannot be prevented, but the amount of damage that occurs can be managed. Tree species, stand age, hardwood content, and drainage class, as well as the outbreak severity and length, determine the amount of tree mortality during budworm outbreaks. Silviculture and forest management can be used to reduce the incidence of the most damaged stand types across the landscape. The amount of defoliation in mixed balsam fir-hardwood stands is strongly negatively related to hardwood content, especially with hardwoods > 40%. The Spruce Budworm Decision Support System (DSS) links models of stand and forest response to budworm outbreaks and inventory interpretation to a GIS, and can be used to evaluate effects of outbreaks and management on forest structure and timber supply. An example using the Spruce Budworm DSS for a portion of the Fundy Model Forest indicated that losses from a future budworm outbreak could be reduced 34% by directing harvesting and silviculture towards conversion of one-half of the most vulnerable stand types into low susceptibility or non-susceptible species. Key words: decision support system, vulnerability, silviculture, insect damage

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