Düzce bölgesi taşkın duyarlılık alanlarının belirlenmesi

OĞUZ, Esin; Oğuz, Kahraman; ÖZTÜRK, Kurtuluş

doi:10.29128/geomatik.972343

Cited by 15 publications

(4 citation statements)

References 4 publications

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“…River networks and basin boundaries can be determined via ArcGIS and Basin toolbox, which are GIS software. GIS is used to solve many water engineering problems and to prepare base data such as digital elevation model data [17,22,[24][25][26][27][28][29][30][31].…”

Section: Methodsmentioning

confidence: 99%

Modeling of annual maximum flows with geographic data components and artificial neural networks

Çubukçu

Demir

Sevimli

2023

International Journal of Engineering and Geosciences

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Disasters such as floods and floods are also encountered on the days when the highest flow is recorded, according to the Annual Maximum Flow (AMF) statistics. The Annual Maximum Flow is the highest flow rate ever recorded in a water year. Wherever this flow happens, it usually results in flooding. Snow melts and unexpected precipitation associated with temperature fluctuations are the two most important factors that create flooding. The deluge that follows kills people and destroys property in communities and agricultural lands. As a result, it's critical to predict the flow that causes flooding and take appropriate precautions to limit the damage. The prediction of the probability of the flood event in advance is very important for the safety of life and property of large masses and agricultural lands. Early warning systems, disaster management plans and minimizing these losses are among the important goals of the country's administration. In this study, It is used in five Current Observation Stations (COS) located in Yeşilırmak Basin in Turkey. By using 8 input data including geographical location, altitude and area information of these stations, AMF data were tried to be estimated for each COS. A total of 240 input data was used in the study. The data period covers the years 1964-2012. Unfortunately, AMF values cannot be monitored for all 5 stations used after 2012.Therefore, the data period was stopped in 2012. In this study, Multilayer Artificial Neural Networks (MANN), Generalized Artificial Neural Networks (GANN), Radial Based Artificial Neural Networks (RBANN) and Multiple Linear Regulation (MLR) methods were used. Input data sets were made into 4 packets and these packages were used respectively in both training and testing stages. Root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) were used as the comparison criteria. The results are as follow; MANN (8 Input) (RMSE = 119.118, MAE = 93.213, R = 0.808), and RBANN (2 Input) (RMSE = 111.559, MAE = 81.114, R = 0.900). These results show that AMF can be predicted with artificial intelligence techniques and can be used as an alternative method.

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Section: Methodsmentioning

confidence: 99%

Modeling of annual maximum flows with geographic data components and artificial neural networks

Çubukçu

Demir

Sevimli

2023

International Journal of Engineering and Geosciences

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“…Information on the criteria (DEM) was collected using remote sensing methods and analyzed with GIS (Das & Pardeshi, 2018;Wang et al, 2019;Das, 2020;Shekar & Mathew, 2023). Oğuz et al, (2016) In the study conducted by A, according to the multicriteria decision-making method at the Artvin province scale, the land use, slope, soil, geology, geology, aspect, distance to the river and maximum precipitation maps of the areas were graded according to the CCA method in GIS environment and the flood risk map was determined in GIS environment. According to the risk map obtained, it is stated that the areas with "very high" and "high" flood risk are located in the residential area and these areas are the places with the highest probability of being damaged in a possible flood, and the flood risk is higher in the parts of the study area close to the sea, where the slope is low, the rainfall is high and the agricultural areas are common, and that the inclusion of hydrological modeling in the study in future studies will lead to more accurate results.…”

Section: The Flood Risk Analysis Using Ahpmentioning

confidence: 99%

AHP ile Taşkın Risk Analizi ve Doğal Taşkın Yönetiminde Ormanların Rolü: Türkiye'nin Kuzeyinden Bir Vaka Çalışması

AKSOY

2023

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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Aim of studty: The aim of this study is to determine the flood risk map of the study area where floods and flood events are frequently encountered by AHP method. Study area: The study was carried out within the boundaries of the Sinop Regional Directorate of Forestry, Ayancık Forest Management Directorate. Material and method: The flood risk map of the study area was produced by Analytical Hierarchy Process (AHP) method. For AHP, 6 different criteria were used: slope, precipitations, aspect, stream distance, land use, and soil. Forest type maps of the study area were used to analyze the impact of forests on flood risk. In terms of forest structure, the stand structure was divided into 3 classes as coniferous, broadleaf, and mixed forest. Main results: The results showed that flood risk varies with forest structure. Coniferous forest class was determined as the class with the lowest flood risk and mixed forest as the class with the highest flood risk. Research highlights: It was determined that the flood risk changed according to the forest structure. Coniferous forest class was determined as the class with the least flood risk, and mixed forest was determined as the class with the highest flood risk.

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“…Oğuz ve arkadaşları, 2016 yılında yaptıkları çalışmada CBS teknikleri ile Artvin ilinin taşkın risk alanları belirlemişlerdir. Bu alanlarda taşkın olayının sıkça görüldüğü belirtilmiştir [11].…”

Section: Introductionunclassified

Kuru Derelerde Taşkın Risk Analizi; Kebendibi Deresi Örneği

Altın,

Taşkın,

Yurtal

et al. 2024

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Taşkın risk analizi için çeşitli yöntemler kullanılmaktadır. Taşkın risk analizi üzerine yapılan çalışmalar coğrafi bilgi sistemleri üzerine odaklanmıştır. Taşkın riskinin belirlenmesi genellikle açık kanal hesaplamasına dayanmaktadır. Bu çalışmada Hec-Ras paket programı ile tek boyutlu analiz yapılmıştır. Kebendibi Nehri alt havzasındaki taşkın risk analizinin sonuçları sunulmuştur. Raster veriler Netcad programı kullanılarak Mersin ilinin sayısal haritalarından elde edilmiş ve Hec-Ras programında analiz edilmiştir. Analiz sonuçları taşkın risk haritalarının oluşturulmasında kullanılmıştır.

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Düzce bölgesi taşkın duyarlılık alanlarının belirlenmesi

Cited by 15 publications

References 4 publications

Modeling of annual maximum flows with geographic data components and artificial neural networks

Modeling of annual maximum flows with geographic data components and artificial neural networks

AHP ile Taşkın Risk Analizi ve Doğal Taşkın Yönetiminde Ormanların Rolü: Türkiye'nin Kuzeyinden Bir Vaka Çalışması

Kuru Derelerde Taşkın Risk Analizi; Kebendibi Deresi Örneği

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