Journal of Engineering Geology
نشریه زمین شناسی مهندسی
Journal of Engineering Geology
Basic Sciences
http://jeg.khu.ac.ir
1
admin
2228-6837
2981-1600
doi
fa
jalali
1396
5
1
gregorian
2017
8
1
11
1
online
1
fulltext
fa
ارزیابی کارایی روشهای ارزش اطلاعات، تراکم سطح، LNRF و نسبت فراوانی در پهنهبندی خطر زمینلغزش در منطقه پشتدربند، کرمانشاه
Comparative Study of Information Value, Density Area, LNRF, Frequency Ratio Mmethods in Landslide Zoning at Poshtdarband Region, Kermanshah Province
ژئوتکنیک (مکانیک خاک و سنگ)
Geotecnic
مقاله پژوهشی
Original Research
<p>زمینلغزش به سبب امکان رخداد آن در محیطهای طبیعی مختلف و به سبب عوامل طبیعی و مصنوعی متفاوت موثر برآن، نسبت به سایر مخاطرات طبیعی جایگاه خاصی دارد. زمینلغزش ها به علت فراوانی رخدادشان، هر ساله موجب خسارات سنگینی می گردند که جبران برخی از آنها هزینه و وقت فراوانی میطلبد. به این سبب مطالعه و بررسی این پدیده مخصوصا در مناطقی که در مسیر پیشرفت و توسعه قرار دارند، امری ضروری است. از بهترین روشهای مطالعه زمینلغزش ها پهنهبندی خطر آن می باشد، که از دیرباز مورد توجه محقیقن بوده است. در این روش منطقه مورد بررسی با توجه به عوامل موثر در بروز پدیده زمینلغزش، به پهنههای با خطر کم تا خطر خیلی زیاد تقسیمبندی می شوند. این پهنهبندی می تواند در برنامهریزی های منطقهای کمک شایانی داشته باشد. روشهای متفاوتی برای این منظور ارائه و توسعه داده شده است. در این نوشتار از چهار روش ارزش اطلاعات، تراکم سطح، LNRF و نسبت فراوانی که از روشهای آماری دو متغیره می باشند، در پهنهبندی خطر زمینلغزش منطقه پشتدربند استفاده شده است. نتایج بیانگر آن است که روش نسبت فراوانی با اختلاف جزئی نسبت به روش تراکم سطح از میان روشهای بکار برده شده برای پهنهبندی منطقه نتایج مطلوب تری بدست می دهد.</p>
<a href="./files/site1/files/5Extended_Abstract.pdf">./files/site1/files/5Extended_Abstract.pdf</a> <strong>Extended Abstract</strong><br>
(Paper pages 91-114)<br>
<strong>Introduction</strong><br>
Due to possibility of occurrence in various natural environments and the variety of natural and artificial factors that affect landslides, landslide has special importance in natural hazards. Depending on the landform, several factors can cause or accelerate the landslide. According to previous researches, Human activities, land morphology, geological setting, slope, aspect, climate conditions, proximity to some watershed features such as rivers and faults are the most important parameters. Landslides occur frequently each year and they can cause heavy losses which compensating some of them requires a lot of money and time.<br>
Assessing landslide related hazards with only limited background information and data is a constant challenge for engineers, geologists<span dir="RTL">,</span> planners, landowners, developers, insurance companies, and government entities.<br>
The landslide occurrence in terms of time and place are not easily predictable, for this reason, Landslide Hazard Zonation (LHZ) or Landslide Susceptibility Zonation (LSZ) maps are used to predict the happening of landslides. A landslide susceptibility map depicts areas likely to have landslides in the future by correlating some of the principal factors that contribute to landslides with the past distribution of slope failures. These maps are basic tools for land-use planning<span dir="RTL">,</span> especially in mountain areas<span dir="RTL">.</span> Landslide susceptibility mapping relies on a rather complex knowledge of slope movements and their controlling factors<span dir="RTL">.</span> The reliability of landslide susceptibility maps mostly depends on the amount and quality of available data, the working scale and the selection of the appropriate methodology of analysis and modeling.<br>
Such maps are obtained by dividing of a region into near-homogeneous domains and weighting them according to the degree of possible hazard of a landslide. There are two ways to do landslide hazard zonation: (i) a qualitative approach that is based on expert knowledge of the target area and portrays susceptibility zoning in descriptive terms; and, (ii) a quantitative approach based on statistical algorithms. In the present study of landslide susceptibility zonation, bivariate statistical methods (information value, density area, LNRF, frequency ratio) were used. In bivariate statistical analysis, each factor map is combined with the landslide distribution map and weighting values based on landslide densities are calculated for each parameter class.<br>
<strong>Materials and Methods</strong><br>
The best method for studying landslides, which has long been of interest to researchers, is hazard zonation. In this method due to the affecting factors in landslide occurrence, the study area is classified into areas with low to very high risk. Such zonation could be of great help in regional planning. Different methods have been developed for this purpose. In this research four bivariate statistical methods namely information value, density area, LNRF, and frequency ratio are used to investigate the hazard zonation in Poshtdarband region, Kermanshah province. The study began with the preparation of a landslide inventory map. The instability factors used in this study included geology, land use, normalized difference moisture index (NDMI), slope gradient, aspect, distance from faults, distance from surface water, distance from roads, profile curvature and plan curvature. Landslide area ratio was calculated in classes of effective factors maps and weighted by four bivariate statistical methods. In addition, landslide hazard zonation maps were obtained from algebraic sum of weighted maps with regard to breakpoints of frequency curve. Finally, by using density ratio (D<sub>r</sub>) Index through all four methods hazard classes were compared and with the help of quality sum (Q<sub>s</sub>) and precision (P) indexes these four methods were compared and evaluated.<br>
<strong>Results and Discussion</strong><br>
If the landslide susceptibility analyses are performed effectively, they can help engineers, contractors, land use planners, etc. minimize landslide. In this study, bivariate statistical methods were applied to generate landslide susceptibility maps using the instability factors. The bivariate approach computes the frequency of landslides with respect to each input factor separately, and the final susceptibility map is a simple combination of all the factors irrespective of their relative significance in causing landslides in a particular region.<br>
In table 1 subclasses of instability factors which had the highest value in different methods, are summarized.<br>
The density ratio indexes (D<sub>r</sub>), quality sum indices (Q<sub>s</sub>) and precision indices (P) were used to compare the methods. By overlaying the landslide inventory map of the study area and landslide hazard zonation maps, quality sum (Q<sub>s</sub>) and precision (P) indices introduce a suitable model for the studied region, and density ratio index (D<sub>r</sub>) introduces division precision among the zones or hazard classes in each zonation model.<br>
<strong>Table1. subclasses of instability factors in different methods which had the </strong><br>
<strong>highest value</strong>
<table align="center" border="1" cellpadding="0" cellspacing="0">
<tbody>
<tr>
<td nowrap="nowrap" style="height:19px;"><strong> factor methods<span dir="RTL"></span></strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>aspect<span dir="RTL"></span></strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>Slope</strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>distance from surface water </strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>land use </strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>plan curvature</strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>profile curvature</strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>distance from fault</strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>distance from the roads</strong></td>
<td nowrap="nowrap" style="height:19px;"><strong>NDMI </strong></td>
</tr>
<tr>
<td nowrap="nowrap" style="height:19px;">information value<span dir="RTL"></span></td>
<td nowrap="nowrap" style="height:19px;">N, NE<span dir="RTL"></span></td>
<td nowrap="nowrap" style="height:19px;">>40</td>
<td nowrap="nowrap" style="height:19px;">>1000</td>
<td nowrap="nowrap" style="height:19px;">forest</td>
<td nowrap="nowrap" style="height:19px;">concave</td>
<td nowrap="nowrap" style="height:19px;">concave</td>
<td nowrap="nowrap" style="height:19px;"><500</td>
<td nowrap="nowrap" style="height:19px;">>1000</td>
<td nowrap="nowrap" style="height:19px;">-0.17_ -0.408</td>
</tr>
<tr>
<td nowrap="nowrap" style="height:19px;">density area</td>
<td nowrap="nowrap" style="height:19px;">N, NE<span dir="RTL"></span></td>
<td nowrap="nowrap" style="height:19px;">>40</td>
<td nowrap="nowrap" style="height:19px;">>1000</td>
<td nowrap="nowrap" style="height:19px;">forest</td>
<td nowrap="nowrap" style="height:19px;">concave</td>
<td nowrap="nowrap" style="height:19px;">concave</td>
<td nowrap="nowrap" style="height:19px;"><500</td>
<td nowrap="nowrap" style="height:19px;">>1000</td>
<td nowrap="nowrap" style="height:19px;">-0.17_ -0.408</td>
</tr>
<tr>
<td nowrap="nowrap" style="height:19px;">LNRF<span dir="RTL"></span></td>
<td nowrap="nowrap" style="height:19px;">SW, S<span dir="RTL"></span></td>
<td nowrap="nowrap" style="height:19px;">10-20</td>
<td nowrap="nowrap" style="height:19px;">>1000</td>
<td nowrap="nowrap" style="height:19px;">pasture</td>
<td nowrap="nowrap" style="height:19px;">Convex</td>
<td nowrap="nowrap" style="height:19px;">convex</td>
<td nowrap="nowrap" style="height:19px;"><500</td>
<td nowrap="nowrap" style="height:19px;">>1000</td>
<td nowrap="nowrap" style="height:19px;">-0.17_ -0.408</td>
</tr>
<tr>
<td nowrap="nowrap" style="height:19px;">frequency ratio<span dir="RTL"></span></td>
<td nowrap="nowrap" style="height:19px;">N, NE<span dir="RTL"></span></td>
<td nowrap="nowrap" style="height:19px;">>40</td>
<td nowrap="nowrap" style="height:19px;">>1000</td>
<td nowrap="nowrap" style="height:19px;">forest</td>
<td nowrap="nowrap" style="height:19px;">concave</td>
<td nowrap="nowrap" style="height:19px;">concave</td>
<td nowrap="nowrap" style="height:19px;"><500</td>
<td nowrap="nowrap" style="height:19px;">>1000</td>
<td nowrap="nowrap" style="height:19px;">-0.17_ -0.408</td>
</tr>
</tbody>
</table>
<div style="clear:both;"></div>The density ratio for information value method in the very high hazard class is accounted 1.700495. These values for density area, frequency ratio, and LNRF methods are, 3.407827, 3.402257, and 1.694628 respectively.<br>
Method precision (P) values for information value, density area, frequency ratio, and LNRF methods are 0.160826, 0.241024, 0.240672 and 0.16942 respectively.<br>
<strong>Conclusion </strong>
<ul>
<li>Frequency ratio, density area and information value methods showed that forest land use, slope and slope shape factors have the highest impacts on a landslide occurrence.</li>
<li>The LNRF method showed that geology factors, pasture land use and distance from surface water had the greatest role in landslide making.</li>
<li>For frequency ratio, information value, and density area methods, the effective factors in landslide are the same, however through the LNRF method, the three factors which have the greatest impact on landslide happening, are generally different from the three other methods.</li>
<li>The density ratio values show that density area and frequency ratio methods respectively have more accuracy and applicability within all used methods for separating hazard classes in the study area.</li>
<li>The quality sum (Q<sub>s</sub>) results indicate that although there are minor differences, the frequency ratio compared to the density area method was more accurate and more applicable for separating landslide hazard in the Poshtdarband region.</li>
<li>The calculated results of P index indicated that among the used methods, the density area method with a nuance of the frequency ratio method is the most suitable method for the study area.</li>
</ul>
پهنهبندی خطر زمینلغزش, روشهای آماری دو متغیره, پشتدربند
Landslide zonation, Information value, Density area, LNRF, Frequency ratio, Bivariate statistical methods, Poshtdarband area.
91
114
http://jeg.khu.ac.ir/browse.php?a_code=A-10-1018-1&slc_lang=fa&sid=1
Mohammad Hosein
Ghobadi
محمدحسین
قبادی
10031947532846001672
10031947532846001672
Yes
دانشگاه بوعلیسینا همدان، گروه زمینشناسی
Seyed Hosein
Jalali
سیدحسین
جلالی
10031947532846001673
10031947532846001673
No
دانشگاه بوعلیسینا همدان، گروه زمینشناسی
Bahman
Saedi
بهمن
ساعدی
10031947532846001674
10031947532846001674
No
دانشگاه بوعلیسینا همدان، گروه زمینشناسی
Noshin
Pirouzinajad
نوشین
پیروزی نژاد
10031947532846001675
10031947532846001675
No
دانشگاه تبریز