Future research and potential measurement of flood risk in Tehran using SVM vector support machine algorithm

Subject Areas : Climatology

pooya Amiri ¹ , Mohammad ibrahi Afifi ² , marzieh Mogholi ³

1 - PhD Student in Geography, Urban Planning, Larestan University, Iran, Shiraz
2 - Assistant Professor of Geography, Larestan Branch, Islamic Azad University, Larestan Iran
3 - Associate Professor, Department of Geography, Larestan Branch, Islamic Azad University, Larestan, Iran

Received: 2020-09-17 Accepted : 2021-07-19 Published : 2022-03-21

Keywords: Flood, Tehran, Futurology, Vector Support Machine Algorithm,

Abstract :

Future research involves a set of endeavors that visualize potential futures and plan for them by searching for resources, patterns, and factors of change or stability. Future research reflects how the reality of "tomorrow" is born from the change (or stability) of "today." The increasing trend of floods in recent years indicates that most parts of the country are exposed to destructive floods and the extent of flood damage and loss of life and property has increased. The purpose of this study was to predict the future of floods in Tehran using the svm vector support machine algorithm. The research method is spatial analysis. The data used were prepared in the form of information layers. The criteria used were altitude, slope, direction of slope, land use, distance from the road, distance from the waterway, geology. The IGR index was obtained for 7 effective factors selected for the occurrence of surface floods in the study area to evaluate the predictive ability of the model. The results showed that the weight of geological factors, altitude, land use, slope, slope direction, distance from the road, distance from the river were 0.029, 0.026, 0.025, 0.019, 0.015, 0.009, 0.008, respectively, with the highest values. 0.029 was assigned to the geological factor and 0.008 to the distance from the river. The final zoning map showed that the high susceptibility class to flooding was in the range of 40.63% and the very low sensitivity class was 3.55% in the lowest area.

References:

1-احمدی،فرشاد.رادمنش،فریدون.میرعباسینجفآبادی،رسول( ،.1393):مقایسهروشهایبرنامهریزیدرپیشبینیجریان
روزانهرودخانه.نشریهآبوخاکدانشگاهفردوسیمشهد.جلد28، شماره6.
2-حاتمینژاد،ح.آتشافروز،ن.آروین،( ،.م1396):پهنهبندیخطرسیلبااستفادهازتحلیلچندمعیارهوGISمطالعهموردی:
شهرستانایذه،فصلنامهدانشپیشگیریومدیریتبحران،دوره7، شماره2، صص44-57.
3-حسام،ر.ضرابی،ا.تقوایی،( ،.م1398):پتانسیلسنجیخطرسیلابشهریبارویکردتوسعهشهریایمنشهرگنبدکاووس،
فصلنامهمدیریتمخاطراتطبیعی،دوره6، شماره1، ص32.
4-حسینزاده،محمدمهدی.،نصرتی،کاظم.،ایمنی،سپیده.،(1397):تعیینشمارهمنحنیوبرآوردپتانسیلتولیدروانابحوضه
آبخیزحصارک،نشریهتحقیقاتکاربردیعلومجغرافیاییسالهجدهم،شماره51،صص150-133.
5-راستگو،علی.، (1389):نقشاقلیمدربیابانزاییمنطقهجناح،پایاننامهکارشناسیارشد،دانشگاهآزادلارستان،استادراهنما
دکترپرویزکردوانی.
6-راستگو، علی.، قنبری، عبدالرسول.، انصاریلاری، احمد.، (1398):ارزیابیوپتانسیلسنجیخطروقوعسیلدرشهرجناح با
استفادهازالگوریتمماشینپشتیبانبردارSVM،فصلنامهجغرافیایطبیعی،سالدوازدهم،شماره45، صص107-125.
7-قنواتی،عزتالله.،کرم،امیر.،آقاعلیخانی،مرضیه.،(1391):ارزیابیوپهنهبندیخطررخدادسیلابدرحوضهفرحزاد(تهران)
بااستفادهازمدلفازی،مجلهجغرافیاوبرنامهریزیمحیطیسال23،پیاپی48،شـماره4،صص138-121.
8-قهرودیتالی،منیژه.،درفشی،خهبات،(1394):بررسیآشفتگیدرالگویخطرسیلابدرتهران،تحلیلفضاییمخاطرات
محیطی،سالدوم،شماره2،تابستان1394،صفحات16-1.
9-کریمیفیروزجانی،م.نیسانیسامانی،( ،.ن1397):پهنهبندیریسکپذیریسیلبااستفادهازروستصمیمگیریچندمعیاره
مبتنیبراستدلالشهودیدمپسترشافردرحوزهآبخیزنکا،پژوهشهایجغرافیایطبیعی،دوره50، شماره1، صص74-194.
10-گودرزی.، محمدرضا.، فاتحیفر، آتیه.، (1398):پهنهبندیخطرسیلابدراثرتغییراتاقلیمیتحتسناریو8.5RCPبا
استفادهازمدلهیدرولوژیکیSWATدرمحیطGIS(حوضهآذرشهرچای)، نشریهتحقیقاتکاربردیعلومجغرافیایی،سال
نوزدهم،شماره53، صص99-117

11- Benito, G. (2013): Hazardous Processes: Flooding, Treatise On Geomorphology, No.13, Pp.
243-261.
12- Daffara, P. (2011): Rethinking tomorrow’s Cities: Emerging Issues On City Foresight.
Futures. Https://Doi.Org/10.1016/J.Futures.2011.05.009.
13- Jamei, E (2016): Review On The Impact Of Urban Geometry And Pedestrianlevel Greening
On Outdoor Thermal Comfort, Renewable And Sustainable Energy Reviews, Vol. 54. - Pp.
102-117.
14- Kain, C.L. Rigby, E.H. And Mazengarb, C. (2018): A Combined Morphometric,
Sedimentary, Gis And Modelling Analysis Of Flooding And Debris Flow Hazard On A
Composite Alluvial Fan, Caveside, Tasmania, Sedimentary Geology, Vol. 64, Pp. 286-301.
15- Kheyrizadeh, M., J. Maleki And H. Amounia. (2012): Flood Hazard Zoning Using Anp
Model In Watershed, Case Study: Mardaghchay Watershed. Quantitative Geomorphological
Researches, 3: Pp. 39-56 (In Persian).

16- Petit, C.C And E. F, Lambin. (2020): Impact Of Data Integration Technique On Historical
Land-Use/Land-Cover Change: Comparing Historical Maps With Remote Sensing Data In
The Belgian Ardennes. Landscape Ecology.17:117-132.
17- Ratciffe, John & Krawczyk Ela, (2011): Imagineering City Futures: The Use Of Prospective
Through Scenarios In Urban Olanning, Futures, Pp. 12-14.
18- Rohani, H., M. Mohseni Saravi And A. Malekian. (2015): Identify The Most Important
Climate And Morphometry Factors Affecting Peak Flow And Preparing Regression Model
Of The East And North-East Of Iran. Journal Of Agricultural Sciences And Natural
Resources, 3: Pp. 99-108 (In Persian).
19- Santangelo, N. And Santo, A. (2011): Multiscale Map Analysis In Alluvial Fan Flood-Prone
Areas, Journal Of Maps, Vol. 12, No. 2, Pp. 382–393.
20- Yalcin, A. (2018): Gis-Based Landslide Susceptibility Mapping Using Analytical Hierarchy
Process And Bivariate Statistics In Ardesen (Turkey): Comparisons Of Results And
Confirmations, V. 72, Pp. 1-15.

 

 

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