كليدواژه :
آجرلو چاي , سيلخيزي , پهنه بندي , تصميمگيري چند شاخصه , منطق فازي
چكيده لاتين :
Introduction
Flood is one of the most complex and destructive natural phenomena that causes
huge damage to agriculture, fisheries, housing and infrastructure, and strongly affects
social and economic activities. The northwestern part of the country is due to its
semi-arid and mountainous climatic conditions and, therefore, high rainfall variability,
including areas subject to destructive floods. Therefore, it is important to provide
flood potential mapping maps for Possible disaster management structure floods.
And reducing their effects. There are various methods for determining the amount of
runoff and the zoning of potential winding. Most of these methods are based on
graphing methods and using empirical formulas, flood data analysis, basin dividing
into a number of sub-basins, remote sensing data and GIS, and computer
mathematical models and more from the view of flood production in basins. Be In the
meantime, the use of new multi-indicator decision making methods and its
combination with spatial data in recent years has been considered and used by
experts, more than other methods. The multi-factorial decision-making systems
provide the method and technique needed to analyze complex decision problems,
which often include non-comparable data and criteria. The success of the GIS and
MCDA in the analysis of natural hazards and other environmental studies has already
been proven.
Materials and Methods
In this study, the Ajorlou chay River basin is zoned with the aim of determining flood
zones with the aim of determining flood zones using a new approach to multi-factor
decision-making and fuzzy modeling. The study area is from the sub-branches of the
Zarin-e-rud River in the city of Miandoab, West Azerbaijan Province. In this field,
eight natural and human parameters including rainfall, land use, morphological
characteristics of slopes such as slope of slopes such as gradient of siope, elevation,
vegetation index, distance from main rivers, density of drainage network and
petrology were used to implement the model. in order to correct the required layers
after production of layers of factors and field visits and satellite imagery, the
materials were used. Then, by distributing the questionnaire and collecting the expert
theories, for each of the weight criteria and sub-criteria, using the AHP method then,
through the overlapping of the layers in the GIS environment, using the fuzzy
operators of the flood hazard map It was turned out.
Discussion
In order to be able to use the layers in a fuzzy model, it is necessary to first phase the
layers to be fuzzy according to the desired object based on membership functions.
With fuzzy functions, using some of the functions in version 10 of the ARC GIS
software or formulating in the Raster Calculator, the layers were placed in
standardized layers in the value range of zero to 1. A fuzzy gamma operator has been
used having moderate role in terms of fuzzy and fuzzy multiplication which reduces
the sensitivity of the fuzzy multiplier operator and the very low fuzzy operator•s
sensitivity and brings it closer to reality. It should be kept in mind that the correct
choice of gamma to improve the accuracy of the work is very useful. According to the
studies carried out and their results, and the implementation and comparison of
different gamma values, the result was a 0.7% gamma. With the implementation of
the models, the zoning map of flooding and flood capability was prepared in five
classes.
Conclusion
With the implementation of fuzzy gamma on eight parameters, the flood potential
map of the study area was prepared. The results of three different gamma (0. 77 .0.0
and 0.9) values were evaluated using flood data in the area and finally, by flood
landing and flood mapping It was found that 7.0 gamma had the best result in the
zoning of flood areas. As map 4 shows, the southeastern heights of the basin have
the highest potential for flooding, and are mainly characterized by high, low-lying,
low-permeability, high drainage densities, and thinned vegetation. According to the
map, about 10% of the area with a high potential and 25% has a very high potential
for flooding.
