Integration of spatial fire risk model results into a decision support system – A case study at Laugarvatn, South Iceland

2020 ◽  
Author(s):  
Michaela Hrabalikova ◽  
Björn Traustason ◽  
David Christian Finger
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Decision Support System ◽  
Forest Fire ◽  
Support System ◽  
Large Scale ◽  
Risk Model ◽  
Landscape Planning ◽  
Fire Spread ◽  
Geographical Information

<p>Iceland is well known for its harsh weather, long winters and frequent geologic activity impacting on the build and natural environment. Although wildfires are rather rare in Iceland, their occurrence might reach a disastrous extent as revealed during the wildfire in 2006. Today, one of the main challenges consist of optimisation landscape planning, disaster and risk management by integrating state-of-art fire models, knowledge in geographical information systems (GIS) and remote sensing. In this study, we present the implementation of simulation results in a decision support system for fire protection. For this purpose, the area of Laugarvatn in South Iceland was selected as a pilot area. Lagarvatn is an ideal pilot area with a high concentration of summer houses and camp caravans surrounded by large-scale natural birch forests. The core of the study is forest fire spread analysis using simulation models and the identification of accessible water sources for firefighting. The input parameters were generated from remote sensing data and GIS databases. Forest types, canopy cover, wind direction and speed and other meteorological variables, topographic feature accelerating forest fire were crucial parameters for producing fire spread probability maps. The fire spread scenario maps, water source maps and road network analysis is one of the critical elements in the decision support system.</p>

scholarly journals An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2818
Author(s):  
Hai Sun ◽  
Xiaoyi Dai ◽  
Wenchi Shou ◽  
Jun Wang ◽  
Xuejing Ruan
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Water Body ◽  
Remote Sensing Data ◽  
Flood Inundation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Tracking Model

Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model's performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

scholarly journals A Decision Support System for Public Funding of Experimental Development in Energy Research

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1357 ◽  
Author(s):  
Simon Hirzel ◽  
Tim Hettesheimer ◽  
Peter Viebahn ◽  
Manfred Fischedick
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Public Funding ◽  
Evaluation Process ◽  
Decision Makers ◽  
Market Failures ◽  
Energy Research ◽  
Experimental Development

New energy technologies may fail to make the transition to the market once research funding has ended due to a lack of private engagement to conclude their development. Extending public funding to cover such experimental developments could be one way to improve this transition. However, identifying promising research and development (R&D) proposals for this purpose is a difficult task for the following reasons: Close-to-market implementations regularly require substantial resources while public budgets are limited; the allocation of public funds needs to be fair, open, and documented; the evaluation is complex and subject to public sector regulations for public engagement in R&D funding. This calls for a rigorous evaluation process. This paper proposes an operational three-staged decision support system (DSS) to assist decision-makers in public funding institutions in the ex-ante evaluation of R&D proposals for large-scale close-to-market projects in energy research. The system was developed based on a review of literature and related approaches from practice combined with a series of workshops with practitioners from German public funding institutions. The results confirm that the decision-making process is a complex one that is not limited to simply scoring R&D proposals. Decision-makers also have to deal with various additional issues such as determining the state of technological development, verifying market failures or considering existing funding portfolios. The DSS that is suggested in this paper is unique in the sense that it goes beyond mere multi-criteria aggregation procedures and addresses these issues as well to help guide decision-makers in public institutions through the evaluation process.

Decision support system for corrective maintenance in large-scale photovoltaic systems

2021 ◽  
Author(s):  
Andreas Livera ◽  
Marios Theristis ◽  
Alexios Charalambous ◽  
Joshua S. Stein ◽  
George E. Georghiou
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Photovoltaic Systems ◽  
Corrective Maintenance

A decision support system that links short-term silvicultural operating plans with long-term forest-level strategic plans

1993 ◽  
Vol 23 (6) ◽  
pp. 1078-1095 ◽  
Author(s):  
Robert G. Davis ◽  
David L. Martell
Keyword(s):  
Information System ◽  
Decision Support ◽  
Decision Support System ◽  
Geographical Information System ◽  
Support System ◽  
Planning Horizon ◽  
Geographical Information ◽  
Timber Species ◽  
Short Term

This paper describes a decision support system that forest managers can use to help evaluate short-term, site-specific silvicultural operating plans in terms of their potential impact on long-term, forest-level strategic objectives. The system is based upon strategic and tactical forest-level silvicultural planning models that are linked with each other and with a geographical information system. Managers can first use the strategic mathematical programming model to develop broad silvicultural strategies based on aggregate timber strata. These strategies help them to subjectively delineate specific candidate sites that might be treated during the first 10 years of a much longer planning horizon using a geographical information system and to describe potential silvicultural prescriptions for each candidate site. The tactical model identifies an annual silvicultural schedule for these candidate sites in the first 10 years, and a harvesting and regeneration schedule by 10-year periods for aggregate timber strata for the remainder of the planning horizon, that will maximize the sustainable yield of one or more timber species in the whole forest, given the candidate sites and treatments specified by the managers. The system is demonstrated on a 90 000 - ha area in northeastern Ontario.

Decision Support System for Wind Farm Installation Using Bipolar Analysis

2015 ◽  
pp. 1-21 ◽  
Author(s):  
Yasmina Bouzarour-Amokrane ◽  
Ayeley P. Tchangani ◽  
François Pérès
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Wind Energy ◽  
Support System ◽  
Large Scale ◽  
Wind Farm ◽  
Negative Impact ◽  
Life Quality ◽  
Energy Projects ◽  
And Performance

The necessity to control and reduce the negative impact of human activities on environment and life quality along with technology progress in renewable energy in general and wind energy in particular render it possible today to consider wind energy projects on a large scale. Developing wind energy on a large scale however raises other problems such as choosing an adequate site to settle a wind farm where many other issues such technical feasibility and performance levels, visual pollution, economic and social concerns, etc. must be addressed. Such decisions usually involve many parameters and necessitate the collaboration of many stakeholders. In this context, this chapter proposes an approach based on the concept of bipolar analysis through Benefit Opportunity Cost and Risk (BOCR) analysis, which permits one to address correctly a Group Decision-Making Problem (GDMP) to build a decision support system in order to assist the wind farm installation process.

Pragmatism in Decision Support System Research

2021 ◽  
pp. 1471-1493
Author(s):  
Mohammad Tafiqur Rahman
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Humanitarian Logistics ◽  
Knowledge Based ◽  
Relief Distribution ◽  
Distribution Process ◽  
Complex Knowledge

Decision making on relief distribution is a complex multidisciplinary task in humanitarian logistics. It incorporates decision makers from different but related problem areas. The failure to perform assigned decision-making tasks in any area makes the entire system unstable and delays the relief distribution process. An organized, well-planned, and practical decision support system (DSS) can assist practitioners in making rapid decisions on delivering relief items. Hence, DSS researchers in humanitarian logistics require rigorous thinking, close and critical analysis, and the identification of challenges to conduct research or validate the generated knowledge properly. To perform such complex knowledge-based tasks, the philosophical understanding of DSS in the humanitarian context is necessary. After analyzing the commonly used philosophical paradigms, this research identifies the pragmatic approach as the adequate support for solving decision-making problems in relief distribution during large-scale disasters.

An Integrated Decision Support System for Intercropping

2010 ◽  
Vol 2 (3) ◽  
pp. 51-66 ◽  
Author(s):  
A. S. Sodiya ◽  
A. T. Akinwale ◽  
K. A. Okeleye ◽  
J. A. Emmanuel
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Crop Production ◽  
Large Scale ◽  
Weather Condition ◽  
Agricultural Practice ◽  
Pests And Diseases ◽  
General Decline ◽  
Fertility Protection

Intercropping, which is the agricultural practice of growing two or more crops in the same land area, is not currently yielding adequate results in Africa. Despite the advantages of intercropping like improved soil fertility, protection against pests and diseases and eventual increase in farm yield, this farming practice is faced with challenges—inadequate planning, bad crop management and lack of required intercropping expertise. Consequently, this has resulted in inadequate reward for farmers and a general decline in crop production. In this regard, the authors present an Intelligent and Integrated Intercropping Decision Support System for Intercropping (IDSS-I) for improved crop production. The design adopts a forecasting component that provides farmers with the estimated yield and income depending on the size of land, soil type and weather condition. Although the implementation was carried out using JAVA and SQL, usability testing revealed 85% acceptance of the tool among the contacted 10 large scale farmers. It was also confirmed that the system provided 95% diagnosis information for 90% common Africa crop diseases.

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