prilagoðeno pretraživanje po punom tekstu

ŠUMARSKI LIST 7-8/2019 str. 40     <-- 40 -->        PDF

areas (Dilley et al. 2005). The landslide is force of natural and also triggered by environmental events, such as earthquake (Evans et al. 2009), high rainfall and large waves (Hapke and Green 2006), (typhoon-induced floods) Acosta et al. 2016), forest loss (Bathurst et al. 2007, Pfeil-McCullough et al. 2015). In addition to, landslide, adversely affects the environment and people (Brabb 1991, Petley 2012, Van der Geest 2018, Zumpano et al. 2018). As such, it is of great importance to determine landslide sensitive areas in advance.
Monitoring, determination of effective factors and modelling are required for take measures against landslide. In this context, in recent years, an increasing number of Landslide Susceptibility Mapping (LSM) (Corominas et al. 2014) studies have been carried out in many countries all around the world (i.e. Austria, China, India, Iran, Ireland, Italy, Korea, Nepal, Portugal, Taiwan, Turkey, and USA). In these studies, many different modelling were developed via Geographic Information System (GIS) and Remote Sensing (RS) techniques such as Logistic Regression (LR) (Eker and Aydın 2016; Lin et al. 2017; Pourghasemi et al. 2018), Adaptive Neuro Fuzzy Inference System (ANFIS) (Bui et al. 2012; Aghdam et al. 2016; Jaafari et al. 2017),  Frequency Ratio (FR) (Lee and Talib 2005; Lee et al. 2015), Kernel Logistic Regression (KLR)- Alternating Decision Tree (ADT)- Support Vector Machine (SVM) (Yao et al. 2008; Hong et al. 2015), Step-wise Weight Assessment Ratio Analysis (SWARA) (Dehnavi et al. 2015), Analytic Hierarchy Process (AHP) (Ercanoğlu et al. 2008; Shahabi et al. 2014), Artificial Neural Networks (ANN) (Ermini et al. 2005; Choi et al. 2012; Conforti et al. 2014), Weighted Linear Combination (WLC) (Feizizadeh and Blaschke 2013), Ordered Weighted Average (OWA) (Feizizadeh and Blaschke 2013), bivariate statistics (BS) (Yalçın et al. 2011), Statistical Index (Wi) (Yalçın et al. 2011; Aghdam et al. 2016), Fuzzy Logic (FL) (Akgün and Türk 2010; Akgün et al. 2012; Aksoy and Ercanoğlu 2012), Back Propagation Algorithm (BPA) (Vahidnia et al. 2010), Weighting Factor (Wf) (Yalçın 2008), GIS Based Road-Pegging Tool (PEGGER) (Jaafari et al. 2015), Bayesian (Jaafari et al. 2015), Modified- Analytic Hierarchy Process (M-AHP) (Nefeslioğlu et al. 2012), Machine Learning (ML) (Steger et al. 2016; Kavzoglu et al. 2019), Multi-layer Perceptron Neural Network (MLP-NN) (Pham et al. 2017), Logistic Regression (GLM)- Generalized Additive Models (GAM), Weights of Evidence (WoE)- Support Vector Machine (SVM)- Random Forest Classification (RF)- Bootstrap Aggregated Classification Trees (Bundling) with penalized Discriminant Analysis (BPLDA) (Goetz et al. 2015), Logistic Model Tree (LMT) (Truong et al. 2018), Prompt Assessment of Global Earthquakes for Response (PAGER) (Tanyaº et al. 2017). Due to the climatic-topographic-social characteristics, the factors used in these models vary.
Landslides take place by actuation of various factors such as elevation (Gorsevski et al. 2006; Lu et al. 2011; Feizizadeh and Blaschke 2013; Eker and Aydın 2016), slope (Pantha et al. 2008; Nefeslioğlu et al. 2012; Dehnavi et al. 2015; Lee et al. 2015; Martinovic et al. 2016), aspect (Vahidnia et al. 2010; Hong et al. 2015), lithology (Conforti et al. 2014; Jaafari et al. 2015; Zezere et al. 2017), distance to faults (Saha et al. 2005; Vahidnia et al. 2010), distance to streams (Yalçın et al. 2011; Pham et al. 2017), distance to roads (Yalçın 2008; Shahabi et al. 2014; Steger et al. 2016), Topographic Wetness Index (TWI) (Goetz et al. 2015; Jacobs et al. 2018) and Stream Power Index (SPI) (Akgün and Türk 2010; Conforti et al. 2014).
The aim of this study is the determination of the most appropriate model among the different models in the planning of forest road network in the landslide sensitive areas. For this purpose, 12 models were developed using three different approaches, M-AHP, FIS, and LR. In the solution process, the models were generated by evaluating specific factors such as elevation, slope, aspect, lithology, distance to stream, distance to roads, TWI, and SPI.
Study Area – Prostorno podruèje
The study area is İhsangazi Watershed in İhsangazi district of Kastamonu province located in the northwest of Turkey. İhsangazi Watershed has an area of 21,863 ha and it is located between the latitude of 41°12’ 01’’ and 41°02’ 31’’ and longitude of 33°31’ 36’’ and 33°39’ 25’’ (Figure 1). The study