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Showing 2 results for Analytical Network Process

Dr Fariba Esfandiary Darabad, Sedigheh Layeghi, Dr Raoof Mostafazadeh, Khadijeh Haji,
Volume 8, Issue 2 (9-2021)
Abstract


The zoning of flood risk potential in the Ghotorchay watershed with ANP and WLC multi-criteria decision making methods
 
 
Extended Abstract
Introduction
Flood is one of the most complex and natural destructive phenomena that have many damage every year. The northwestern region of the country, due to its semi-arid and mountainous climate and thus of high rainfall variability, is one of the areas exposed to destructive floods. Flood risk zoning is an essential tool for flood risk management. Therefore, the purpose of this research was to determine the flood risk zones in the Ghotorchay watershed by using the analytical network process (ANP).
 
Methodology
In this research,, with geographic information system (GIS), satellite images, synoptic station data, analytical network process and the combination of layers, the flood potential of has been modeled in the Ghotorchay watershed. The final map of flood risk based on a combination of factors and climatic and physical elements including land use, geology, vegetation, topography, slope and land capability was prepared. The weight of each criterion was determined by ANP method and used by weighted linear composition (WLC) method for spatial modeling and incorporation of layers.
 
Results
The results of flood risk zoning showed that the Qal layers from geology, slopes of less than 3 precent, land capacity of units 5, 6 and 7, and as well as poor vegetation cover were identified as flood zones. The results obtained from the analytical network process model indicate the fact that part of the watershed is affected by the risk of flooding with the very high potential, which is mainly located in the downstream of watershed. For this reason, the streams of rank 3 and 4 are considered as flood zones and flood guide areas to the downstream areas. Also, river networks of 5 and higher ranks are in the range of floodplains or river coastal and usually have surface and extensive floods.
 
Conclusion
The flood prone areas and providing effective solutions for flood management is one of the main steps in reducing flood damage. Therefore more precise management and control of basins with multiple dams, embedding flood alert systems in flood plain areas and performing basic measures is one of the most urgent measures to prevent, improve and control this natural disaster.
Key words: Analytical network process, Biological protection, Floodplain, Flood risk assessment, Ghotorchay
 
Negar Hamedi, Ali Esmaeily, Hassan Faramarzi, Saeid Shabani, Behrooz Mohseni,
Volume 11, Issue 2 (8-2024)
Abstract

Forest fire in many ecosystems is a natural phenomenon, but also a serious and dangerous threat with environmental, ecological, and physical effects. Therefore, this research investigated the risk areas of fire in Zagros forests identification to evaluate the changes in the time series of deals with a potential fire hazard. To achieve this goal fuzzy layers of analysis network process and order weighted average method were used regularly. For this purpose, fire Zagros forests using satellite images Landsat and MODIS Lordegan city in the period between 2000, 2007, and 2014 and the factors affecting fire are examined. The high-risk areas based on classification utility area and the number of zones were identified as fire-prone areas. In the analytical network process procedure, the largest weighs were assigned to the distance from residential areas and roads, GVMI index, and maximum daily air temperature factors which were 0.209, 0.198, 0.09, and 0.0716, respectively. Time series analysis map showing the extent of critical areas from 2000 to 2014 decreased by investigating the factors affecting fire occurrence in critical areas, distance for roads and residential areas, slope, aspect, GVMI index, and NDVI and maximum temperatures have the greatest impact were on fire. The low-risk scenario and a small amount of compensation with the ROC higher than 0.7 as the best model was the estimated risk of forest fires. The preparation of a map of areas susceptible to fire, as well as analyzing and analyzing the time series of factors affecting the fire in different years, is an effective step in helping forest managers to plan and implement preventive operations in high-risk areas.
 

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