Flood susceptibility mapping using GIS and multicriteria decision analysis: Saricay-Çanakkale (Turkey)

Tiryaki, M.; Karaca, Öznur

doi:10.1007/s12517-018-3675-3

Cited by 21 publications

(15 citation statements)

References 44 publications

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“…In recent years, researchers have suggested, so many broader models for flood risk assessment incorporate remote sensing and GIS tools, which provided considerable good accuracy (Pradhan 2009;Bates 2012;Wanders et al 2014;Nikoo et al 2016). Most of the previously used models were mainly focused on the hydrodynamic model, hydrological model, multi-criteria decision analysis (MCDA), statistical models (SM), and machine learning (ML) techniques, which are incorporated in the geographic information system (Singh and Kumar 2013;Pradhan 2014;Elkhrachy 2015;Vojtek and Vojteková 2016;Rosser et al 2017;Samanta et al 2018;Tiryaki and Karaca 2018;Liuzzo et al 2019;Santos et al 2019;Tehrany et al 2019a;Shahabi et al 2020). The most commonly used models and techniques concerning flood susceptibility mapping include frequency ratio (FR) (Rahmati et al 2015;Shafapour Tehrany et al 2017;Samanta et al 2018;Tehrany et al 2019a;Sarkar and Mondal 2020), analytical hierarchy process (AHP) (Elkhrachy 2015;Dahri and Abida 2017;Das 2018;Rahman et al 2019;Sepehri et al 2020a), shannon's entropy (Khosravi and Pourghasemi 2016;, weights of evidence (WoE) (Tehrany et al 2014b;Rahmati et al 2015;Shafapour Tehrany et al 2017;Costache 2019), artificial neural networks (ANN) (Kia et al 2012;Ruslan et al 2013;Elsafi 2014;Rahman et al 2019;Kordrostam...…”

Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Ghosh

Sutradhar

Das³

et al. 2021

Preprint

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Flood is a typical natural disaster which every year causes enormous damage to the natural environment, buildings and victims worldwide. The efficiency of flooding depends upon numerous criteria: flood strength, amplitude, frequency, flow time, changes in plan and river geometry, etc. In this research, three statistical models such as the frequency ratio, Shannon's entropy, and weight of evidence have been used to identify flood susceptibility regions in the Kopai River Basin. In order to run these statistical models, flooded and non-flooded pixels were employed. The outcome of these three models demonstrates that the upper reach of the basin is characterised by non-flooded zones and the bottom reach of the basin is high flooded zones. The lower part of the basin is therefore more vulnerable than that of the upper portion. 11 thematic layers are employed to get the final output of flood susceptibility zones. e.g., soil type, normalised vegetation difference index, normalised water differential index, river lift, slope, drainage density, land usage and land cover, rainfall, soil moisture indices, surface roughness. 370 sample points have been taken as test data and finally receiver operating curves have been plotted for testing the degree of accuracy. The result of the validation reveals that the accuracy of the frequency ratio is 96.5%. Shannon entropy is 91.2% accurate and the weight of evidence is 97.1% accurate. Weight of evidence is therefore the best model for flood susceptibility zones identification for this Kopai basin.

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Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Ghosh

Sutradhar

Das³

et al. 2021

Preprint

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“…Taşkın duyarlılık haritalarının oluşturulmasında çeşitli niteliksel veya niceliksel yaklaşımlar kullanılmaktadır. Son zamanlarda da CBS ve çok kriterli değerlendirme yönteminin birlikte kullanıldığı pek çok çalışma dikkatleri çekmektedir (Malczewski, 1999;Malczewski, 2004;Yang vd., 2013;Tiryaki ve Karaca, 2018).…”

Section: Introductionunclassified

“…Farklı bölgeler için taşkın alanlarının haritalanması ve tehlike analizi genellikle coğrafik olarak birbirleriyle ilgili olması gereken çok sayıda kriter içermektedir (Minea, 2013;Poussin vd., 2014;Rahmati vd., 2016). Farklı araştırmacılar yaptıkları çalışmalarda, litoloji, eğim, bakı, yükseklik, arazi kullanım, topografik nemlilik indeksi, akış birikimi ve akarsuya olan mesafe gibi değişik faktörlerin bazılarını çalışma alanlarının taşkına duyarlılıklarını belirlemek ve haritalandırmak için kullanmışlardır (Korah ve Lopez, 2015;Pourali vd., 2016;Tiryaki ve Karaca, 2018;Sözer vd., 2019…”

Section: Introductionunclassified

Çerçi Deresi (Fethiye-Muğla) Taşkın Duyarlılık Alanlarının CBS ile Çok Kriterli Karar Verme Analizi Kullanılarak Haritalanması

Akin¹,

Karaca²

2021

Yerbilimleri/Hacettepe Üniversitesi Yerbilimleri Uygulama Ve Araştırma Merkezi Dergisi

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Dünyada ve Türkiye'de en önemli doğal tehlikelerden biri olan taşkınlar doğal ya da insan müdahalesi sonucu, şiddetine ve oluşum şartlarına bağlı olarak az veya çok olumsuz etki yaratarak afetlere yol açmaktadır. Bu çalışmada, Muğla İli Fethiye yerleşim alanı içerisinde yer alan Çerçi Deresi'nin taşkın analizi yapılarak, dere yatağının da bulunduğu çalışma alanı için taşkın duyarlılık haritası hazırlanmıştır.İnceleme alanı için Çerçi Deresi etrafında taşkına duyarlı alanların haritalanmasında bölgeye ait jeolojik, jeomorfolojik, sedimantolojik ve hidrolojik verilerden yararlanılmıştır. Çalışma alanı zeminlerinin fiziksel özellikleri incelenmiş ve ince kum, silt ve kil boyutundaki sedimanların yoğunlukta olduğu görülmüştür. Alınan örnekler ile yapılan zemin deneyleri neticesinde hakim zemin sınıfının orta plastisiteli inorganik _________________________

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“…people annually [36]. Floods are the natural disasters causing enormous damage to the mankind and environment throughout the globe [76]. Being integral part of hydrological cycle, floods occur due to heavy precipitation and / or extreme flows of rivers inundating the neighbouring floodplains or terrains [5,26,41].…”

Section: Introductionmentioning

confidence: 99%

“…Resilience to potential environmental disasters can be increased through proper urban land use planning [78]. In this, geoinformatics has emerged extensively been used to monitor flood inundation and preparation of management plans [39,59,70,76] urban studies [18,28,45,74] monitoring of flood control, drainage improvement and bank protections, etc. [16,27,79].…”

Section: Introductionmentioning

confidence: 99%

Impact of 2014 Kashmir flood on land use/land cover transformation in Dal lake and its surroundings, Kashmir valley

2020

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Kashmir Valley has witnessed a catastrophic flood in 2014, which led thousands of people homeless and devastated the agricultural lands. In the present study, the impact of 2014 flood occurred during the month of September was analysed in the vicinity of Dal lake using the pre-and post-flood periods satellite observations. The study exhibited an inundation of 42.50 km 2 area (52.47% of total area) during September 2014 flood period, which is primarily topographical low-lying area (i.e. below 1530 m). The land use/land cover (LULC) analysis during pre-and post-flood periods exhibited significant impact over the built-up land with 20.4% decrease in the built-up (from 25.44 to 20.25 km 2), which was mostly evident in the western, southern and eastern parts of Dal lake. It has also severely affected the road network with inundation of 220.84 kms (55.62% of total road network). The road network in the lower elevation (< 1530 m) not only disrupted but also acted as a carrier for the flood water dispersal in the region. The long-term impact and recovery of flood inundation were assessed using spatio-temporal built-up growth during 2014, 2015 and 2018 within the flooded zone through geospatial overlay analysis. The flood (September 2014) affected a total 10.42 km 2 of built-up, wherein the built-up was reduced to 7.50 km 2 , due to the low-lying topography and nearest proximity to flow path of Jhelum river. Later, the built-up was increased to 9.60 km 2 within the flooded zone during 2018 primarily in the southern parts, representing the long-term recovery after the flood aftermath. Although the impact of flood (2014) was evident in both the flooded and non-flooded zones, the built-up growth was reduced significantly in the flooded zone (− 25.18% change) as compared to non-flooded zones (− 17.32% change). Also, the long-term recovery was comparatively higher in the non-flooded zone (31.84% growth) as compared to the flooded zone (28.03% growth). The study necessitates towards implication of effective urban planning method primarily along the major lakes in order to reduce the increasing impact of catastrophic flood.

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Flood susceptibility mapping using GIS and multicriteria decision analysis: Saricay-Çanakkale (Turkey)

Cited by 21 publications

References 44 publications

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Çerçi Deresi (Fethiye-Muğla) Taşkın Duyarlılık Alanlarının CBS ile Çok Kriterli Karar Verme Analizi Kullanılarak Haritalanması

Impact of 2014 Kashmir flood on land use/land cover transformation in Dal lake and its surroundings, Kashmir valley

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