A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation

2005 ◽  
Vol 20 (5) ◽  
pp. 613-621 ◽  
Author(s):  
L.S. Iliadis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Forest Fire ◽  
Support System ◽  
Risk Estimation ◽  
Fuzzy Model ◽  
Fire Risk ◽  
Forest Fire Risk

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.

scholarly journals FIRESIG - SISTEMA DE SUPORTE A TOMADA DE DECISÃO PARA O COMBATE A INCÊNDIOS NO PARANÁ

FLORESTA ◽  
2004 ◽  
Vol 34 (2) ◽  
Author(s):  
Flavio Deppe ◽  
Eduardo Vedor De Paula ◽  
Jackson Vosgerau ◽  
Alexandre Guetter
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Hot Spots ◽  
Vegetation Index ◽  
Risk Index ◽  
Fire Risk ◽  
Civil Defense ◽  
Environmental Disasters

O FIRESIG representa um sistema de suporte a tomada de decisão para o combate a incêndios no Estado do Paraná. O FIRESIG atende as demandas específicas de atividades de monitoramento, prevenção e combate a incêndios. Os usuários institucionais do FIRESIG se referem ao Instituto Ambiental do Paraná (IAP) e a Coordenadoria Estadual de Defesa Civil do Paraná. O FIRESIG oferece ferramentas para: (i) entrada de dados de focos de calor, índice de vegetação e índice de risco de incêndio, (ii) espacialização, visualização e análise de focos de calor, (iii) identificação de recursos e infra-estrutura disponível para combate aos incêndios, (iv) atualização da base de dados dos recursos disponíveis para o combate aos incêndios, (v) determinação de melhores rotas de acesso aos incêndios. A utilização do FIRESIG reduz o tempo de resposta para o combate aos incêndios e auxilia a montagem de estratégias de combate. O FIRESIG é caracterizado como um sistema de suporte a tomada de decisão, robusto e de baixo custo para combate aos incêndios. Além do mais pode ser adaptado para ser utilizado em outros tipos de desastres ambientais. FIRESIG – DECISION SUPPORT SYSTEM FOR FIRE FIGHT IN PARANÁ Abstract The FIRESIG represents a decision support system for fire fight in the Paraná State. The FIRESIG meets specific demands for monitoring, prevention and fire fight. The system’s users are the Paraná Environmental Institute and the Paraná Civil Defense Coordination. The FIRESIG offers several tools for: (i) hot spots, vegetation index and fire risk index data input, (ii) mapping, visualization and hot spots analysis, (iii) identification of available resources and infrastructure for fire fight, (iv) data base update, (v) determination of firefight best routes. The use of FIRESIG reduces fire fight response time and helps the fire fight strategy definition. The FIRESIG can be considered as robust and a low price fire fight system. Additionally, the system can be adapted for use as a decision support system for other environmental disasters.

scholarly journals Decision Support System for Daily and Long Term Operations of the System of Milan, Italy

2016 ◽  
Vol 154 ◽  
pp. 58-61 ◽  
Author(s):  
Quan Pan ◽  
Mario Erik Castro-Gama ◽  
Andreja Jonoski ◽  
Ioana Popescu
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System

Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system

2006 ◽  
Vol 54 (11-12) ◽  
pp. 11-19 ◽  
Author(s):  
M. Aqil ◽  
I. Kita ◽  
A. Yano ◽  
S. Nishiyama
Keyword(s):  
Decision Support ◽  
Process Control ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Fuzzy System ◽  
Public Safety ◽  
Fuzzy Model ◽  
Neuro Fuzzy ◽  
Flood Monitoring

It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The results indicate that the modified neuro-fuzzy model applied to the flood prediction seems to have reached encouraging results for the river basin under examination. The comparison of the modified neuro-fuzzy predictions with the observed data was satisfactory, where the error resulted from the testing period was varied between 2.632% and 5.560%. Thus, this program may also serve as a tool for real-time flood monitoring and process control.

Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada

2010 ◽  
Vol 90 (1) ◽  
pp. 37-53 ◽  
Author(s):  
H. Wang ◽  
G N Flerchinger ◽  
R. Lemke ◽  
K. Brandt ◽  
T. Goddard ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Decision Support ◽  
Decision Support System ◽  
Soil Water ◽  
Support System ◽  
Cropping System ◽  
System Model ◽  
Surface Residue ◽  
Simultaneous Heat

The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) is a widely used modeling package that often simulates wheat yield and biomass well. However, some previous studies reported that its simulation on soil moisture was not always satisfactory. On the other hand, the Simultaneous Heat and Water (SHAW) model, a more sophisticated, hourly time step soil microclimate model, needs inputs of plant canopy development over time, which are difficult to measure in the field especially for a long-term period (longer than a year). The SHAW model also needs information on surface residue, but treats them as constants. In reality, however, surface residue changes continuously under the effect of tillage, rotation and environment. We therefore proposed to use DSSAT-CSM to simulate dynamics of plant growth and soil surface residue for input into SHAW, so as to predict soil water dynamics. This approach was tested using three conventionally tilled wheat rotations (continuous wheat, wheat-fallow and wheat-wheat-fallow) of a long-term cropping systems study located on a Thin Black Chernozemic clay loam near Three Hills, Alberta, Canada. Results showed that DSSAT-CSM often overestimated the drying of the surface layers in wheat rotations, but consistently overestimated soil moisture in the deep soil. This is likely due to the underestimation of root water extraction despite model predictions that the root system reached 80 cm. Among the eight growth/residue parameters simulated by DSSAT-CSM, root depth, leaf area index and residue thickness are the most influential characteristics on the simulation of soil moisture by SHAW. The SHAW model using DSSAT-CSM-simulated information significantly improved prediction of soil moisture at different depths and total soil water at 0-120 cm in all rotations with different phases compared with that simulated by DSSAT-CSM. Key words: Soil moisture, modeling, Decision Support System for Agrotechnology Transfer-Cropping System Model, Simultaneous Heat and Water Model

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