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Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis
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  Full Terms & Conditions of access and use can be found at Download by:  [University of Tehran] Date:  14 October 2015, At: 22:49 Geocarto International ISSN: 1010-6049 (Print) 1752-0762 (Online) Journal homepage: Flood susceptibility mapping using frequency ratioand weights-of-evidence models in the GolastanProvince, Iran Omid Rahmati, Hamid Reza Pourghasemi & Hossein Zeinivand To cite this article:  Omid Rahmati, Hamid Reza Pourghasemi & Hossein Zeinivand (2016) Floodsusceptibility mapping using frequency ratio and weights-of-evidence models in the GolastanProvince, Iran, Geocarto International, 31:1, 42-70, DOI: 10.1080/10106049.2015.1041559 To link to this article: Accepted online: 16 Apr 2015.Publishedonline: 20 May 2015.Submit your article to this journal Article views: 121View related articles View Crossmark dataCiting articles: 1 View citing articles  Flood susceptibility mapping using frequency ratio andweights-of-evidence models in the Golastan Province, Iran Omid Rahmati a  *, Hamid Reza Pourghasemi  b and Hossein Zeinivand c a  Department of Range and Watershed Management Engineering, Lorestan University, Lorestan, Iran;  b  Department of Natural Resources and Environment, College of Agriculture, Shiraz University, Shiraz, Iran;  c  Department of Range and Watershed Management Engineering, Lorestan University, Lorestan, Iran (  Received 10 September 2014; accepted 28 March 2015 )Flood is one of the most devastating natural disasters with socio-economic and envi-ronmental consequences. Thus, comprehensive  󿬂 ood management is essential toreduce the  󿬂 ood effects on human lives and livelihoods. The main goal of this studywas to investigate the application of the frequency ratio (FR) and weights-of-evidence(WofE) models for   󿬂 ood susceptibility mapping in the Golestan Province, Iran. At   󿬁 rst,a  󿬂 ood inventory map was prepared using Iranian Water Resources Department andextensive  󿬁 eld surveys. In total, 144  󿬂 ood locations were identi 󿬁 ed in the study area.Of these, 101 (70%)  󿬂 oods were randomly selected as training data and the remaining43 (30%) cases were used for the validation purposes. In the next step,  󿬂 ood condition-ing factors such as lithology, land-use, distance from rivers, soil texture, slope angle,slope aspect, plan curvature, topographic wetness index (TWI) and altitude were pre- pared from the spatial database. Subsequently, the receiver operating characteristic(ROC) curves were drawn for produced  󿬂 ood susceptibility maps and the area under the curves (AUCs) was computed. The  󿬁 nal results indicated that the FR (AUC = 76.47%) and WofE (AUC = 74.74%) models have almost similar and reason-able results. Therefore, these  󿬂 ood susceptibility maps can be useful for researchersand planner in  󿬂 ood mitigation strategies. Keywords:  󿬂 ood susceptibility mapping; frequency ratio; weights-of-evidence; GIS;Iran 1. Introduction Floods are the most common natural disaster affecting more people in the worldwide(140 million people per year) than all other disasters (WHO 2003). Floods can cause tosocio-economic and environmental consequences, and thus, comprehensive  󿬂 ood man-agement is very vital (Markantonis et al. 2013). Because of the great potential damagesto natural resources, agriculture, transportation, bridges and many other aspects of urban infrastructure,  󿬂 ood control and prevention measures are urgently needed (Billaet al. 2006; Huang et al. 2008; Dang et al. 2011; Alvarado-Aguilar et al. 2012). From sustainable development viewpoint, the  󿬂 ood hazard management is essential,so that governments can prevent as much damage as possible (Kumar et al. 2010; Feng &Wang 2011; Esteves 2013; Schober et al. 2015). In addition,  󿬂 ash  󿬂 oods considered as amajor obstacle to the local development programmes. However, negative consequences of  *Corresponding author. Emails:,  © 2015 Taylor & Francis Geocarto International  , 2015Vol. 31, No. 1, 42  –  70,    D  o  w  n   l  o  a   d  e   d   b  y   [   U  n   i  v  e  r  s   i   t  y  o   f   T  e   h  r  a  n   ]  a   t   2   2  :   4   9   1   4   O  c   t  o   b  e  r   2   0   1   5  󿬂 ood can be applied by integrated approaches to  󿬂 ood hazard management (Masood &Takeuchi 2012; Jourde et al. 2014). Dewan et al. (2007) proposed comprehensive  󿬂 oodhazard management strategies for land-use planning due to the ef  󿬁 cient management of future  󿬂 ood disasters in Greater Dhaka using Synthetic Aperture Radar data and geo-graphical information system (GIS). However, determining the bene 󿬁 ts of   󿬂 ood reductionis dif  󿬁 cult because there are intangible realities and requires a long time to be revealed(Yi et al. 2010).Remote sensing techniques coupling with GIS tools can provide a good platform tocombine, manipulate and analyse the information for the determination of potentialhazard areas very quickly and more ef  󿬁 ciently (Saha et al. 2005; Pradhan et al. 2011; Devkota et al. 2013; Wang et al. 2013; Pourghasemi et al. 2013a, 2014). On the other  hand, GIS has also been used extensively to study  󿬂 ood and associated damages(Sanyal & Lu 2004; Pradhan 2010; Youssef et al. 2011; Kia et al. 2012; Patel & Srivastava 2013).Different methods were developed for natural hazard mapping using statistical meth-ods and GIS techniques in the last decade (van Westen et al. 2003; Ayalew et al. 2004; Althuwaynee et al. 2014). The most common approaches proposed in the literature arefrequency ratio (FR) (Pareek et al. 2010; Pradhan & Lee 2010; Pradhan & Youssef  2010; Pradhan et al. 2011; Lee et al. 2012a; Shafapour Tehrany et al. 2014a), logistic regression (Ayalew & Yamagishi 2005; Lee et al. 2007; Gorum et al. 2008; Pradhan et al. 2008; Choi et al. 2012; Akgun et al. 2012; Ozdemir & Altural 2013; Felicísimo et al. 2013), arti 󿬁 cial neural networks (Lee et al. 2003; Choi et al. 2010; Poudyal et al. 2010; Pradhan & Buchroithner  2010; Pradhan et al. 2010b; Park et al. 2013; Zare et al. 2013; Conforti et al. 2014) and spatial multicriteria evaluation (Sinha et al. 2008; Yalcin 2008; Fernández & Lutz 2010; Ghanbarpour et al. 2013; Pourghasemi et al. 2014). Also, other approaches have been applied in several studies including Dempster   –  Shafer (Tangestani 2009; Park 2011; Mohammady et al. 2012; Pourghasemi et al. 2013c), deci- sion tree (Yeon et al. 2010; Shafapour Tehrany et al. 2013), fuzzy logic (Bui et al. 2012; Pourghasemi et al. 2012b; Sharma et al. 2013; Ramazi & Amini 2014), index-of-entropy (Bednarik et al. 2010; Pourghasemi et al. 2012a, 2012c; Jaafari et al. 2014; Naghibi et al. 2015), analytical hierarchy process (AHP) (Meyer et al. 2009; Pourghasemi et al. 2012b; Zou et al. 2013; Papaioannou et al. 2015) and weights-of-evidence (WofE) (Bonham-Carter  1991; Oh & Lee 2010; Pradhan, Oh and Buchroithner, 2010a; Regmi et al. 2010; Mohammady et al. 2012; Pourghasemi et al. 2013c; Fu et al. 2013; Dube et al. 2014; Regmi et al. 2014). During the past decades, traditional optimization tech- niques, such as linear, nonlinear and dynamic programming, have been applied for   󿬂 oodhazard assessment (Needham et al. 2000; Olsen et al. 2000; Travis & Mays 2008;  Nagesh Kumar et al. 2010; Yazdi & Neyshabouri 2012). However, the main shortcom- ing of these approaches is related to assumptions in the  󿬂 ood modelling becauseobtained results are too unrealistic (Yazdi et al. 2013).Pradhan (2010) applied multivariate logistic regression to risk area delineation and 󿬂 ood susceptibility mapping in Kelantan River Basin, Malaysia. The results showedthat logistic regression model had reasonable accuracy. Lee et al. (2012a) applied FR model for   󿬂 ood susceptibility mapping in Busan, South Korea. The results showed that FR model is very ef  󿬁 cient for   󿬂 ood susceptibility modelling. Chormanski et al. (2011)utilized RS and GIS techniques and water chemistry analysis for   󿬂 ood extent mappingin Biebrza River Lower Basin, Poland. They stated that the new methodology is effec-tive in recognizing inundated areas. Shafapour Tehrany et al. (2014a) used integrated bivariate and multivariate statistical models for   󿬂 ood susceptibility mapping in Busan, Geocarto International   43    D  o  w  n   l  o  a   d  e   d   b  y   [   U  n   i  v  e  r  s   i   t  y  o   f   T  e   h  r  a  n   ]  a   t   2   2  :   4   9   1   4   O  c   t  o   b  e  r   2   0   1   5  South Korea. The results showed the ef  󿬁 ciency of logistic regression in  󿬂 ood suscepti- bility modelling, and it can be improved in the combination with the bivariate probabil-ity methods. Shafapour Tehrany et al. (2014b) also applied a novel ensemble WofE andsupport vector machine (SVM) models for   󿬂 ood susceptibility mapping in Terengganu,Malaysia. Their results proved the ef  󿬁 ciency of the ensemble WofE and SVM methodsover the individual methods.In general, FR and WofE models are mostly used in landslide susceptibilitymapping and other natural hazards, so it is relatively new in  󿬂 ood susceptibilitymodelling.The Golestan Province in Iran is always exposed to  󿬂 ood hazard, because of climatic and physiographic conditions and anthropogenic activities (Omidvar &Khodaei 2008; Sagha 󿬁 an et al. 2008; Ardalan et al. 2009; Abdolhay et al. 2012). On 10 August 2001, an exceptionally  󿬂 ash  󿬂 ood occurred in the Golestan basin and inun-dated large areas that claimed a total of 300 lives, and 4000 buildings was sustainedheavy damage (Shari 󿬁  et al. 2012). Moreover, according to of  󿬁 cial government reports,the direct damage of this  󿬂 ood was estimated at over USD 400 million dollars (Shari 󿬁 & Mahdavi 2001). From 1990 to 2005, Golestan Province has faced to 64  󿬂 oods andthe total number of people directly affected by the  󿬂 ood was estimated to be more than217,000 (Shari 󿬁  et al. 2012). So, the purpose of current research was to assess andcompare  󿬂 ood susceptibility maps produced using two bivariate statistical GIS-basedapproaches, i.e. FR and WofE models in the Golestan Province, Iran. 2. Study area This study is located in the Golestan Province which is situated in the north-eastern part of Iran. The study area lies between the latitudes of 36° 34 ′  to 37° 50 ′  N and thelongitudes of 54° 5 ′  to 56° 8 ′  E (Figure 1). The total area is almost 11,888 km 2 inhab-ited by population number of 1.4 million. Topographically, this region has mountainousarea and  󿬂 at land. Elevation of the study area ranges between  − 147 and 3349 m. Thestudy area is considered to have climate diversity with an average annual precipitationof 450 mm (WRCG 2013). The central and northern regions have a temperate Mediter-ranean climate, and the southern section has a typical mountain climate. The averagedaily minimum temperature is  − 5.5 °C in the winter, and daily maximum temperatureis 33 °C in the summer (WRCG 2013).In last decade, this area has faced with destructive  󿬂 oods, which was chosen as asuitable application site for   󿬂 ood susceptibility mapping. 3. Methodology Figure 2 shows the applied methodology in this study as a  󿬂 owchart. 3.1. Flood inventory map The future  󿬂 ood event in an area can be estimated using analysing the past records of its occurrence (Manandhar  2010). Therefore, a  󿬂 ood inventory map is considered asthe most important factor for prediction of future  󿬂 ood occurrence. In this study, a 󿬂 ood inventory map containing 144  󿬂 ood locations was prepared for Golestan Provinceusing documentary sources of Iranian Water Resources Department (IWRD) and exten-sive  󿬁 eld surveys, i.e. GPS points (period between 2001 and 2009).44  O. Rahmati  et al.    D  o  w  n   l  o  a   d  e   d   b  y   [   U  n   i  v  e  r  s   i   t  y  o   f   T  e   h  r  a  n   ]  a   t   2   2  :   4   9   1   4   O  c   t  o   b  e  r   2   0   1   5  The  󿬂 ood inventory map was divided randomly (Ohlmacher & Davis 2003;Tunusluoglu et al. 2008; Pradhan 2010; Pourtaghi & Pourghasemi 2015) to two data sets such as 70% (101  󿬂 ood locations) and 30% (43  󿬂 ood locations) for training andvalidation, respectively. 3.2. Flood conditioning factors It is essential to determine the effective factors on the  󿬂 ood occurrence in order to pre- pare  󿬂 ood susceptibility maps (Kia et al. 2012). Therefore, ten conditioning factors Figure 1. Flood location map of Golestan Province, Iran. Geocarto International   45    D  o  w  n   l  o  a   d  e   d   b  y   [   U  n   i  v  e  r  s   i   t  y  o   f   T  e   h  r  a  n   ]  a   t   2   2  :   4   9   1   4   O  c   t  o   b  e  r   2   0   1   5
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