Showing papers by "Candan Gokceoglu published in 2011"

Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia

Abstract: The purpose of the present paper is to manifest the results of the neuro-fuzzy model using remote sensing data and GIS for landslide susceptibility analysis in a part of the Klang Valley areas i Malaysia. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. SPOT 5 satellite imagery was used to map vegetation index. Maps of topography, lineaments, NDVI and land cover were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using a neuro-fuzzy model (adaptive neuro-fuzzy inference system, ANFIS) to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary landuse planning purposes. As a conclusion, the ANFIS is a very useful tool for regional landslide susceptibility assessments.

Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network

Abstract: Uniaxial compressive strength of intact rock is significantly important for engineering geology and geotechnics, because it is an important design parameter for tunnels, rock slopes rock foundations, and it is also used as input parameter in some rock mass classification systems. This paper documents the results of laboratory experiments and numerical simulations (i.e. neural network) conducted to estimate the uniaxial compressive strength of some clay-bearing rocks selected from Turkey. Emphasis was placed on assessing the role of slake durability indices and clay contents. The input variables in developed neural network (NN) model are the origin of rocks, two/four-cycle slake durability indices and clay contents, and the output is uniaxial compressive strength. It is shown that the performance of capacities of proposed NN model is quite satisfactory. However, the NN model including four cycle slake durability index yielded slightly more precise results than that including two cycle slake durability index as input parameter. The paper also presents a comparative study on the accuracy of NN model and genetic programming (GP) in the results.

Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey)

Abstract: The main purpose of this study is to develop a new hazard evaluation technique considering the current limitations, particularly for shallow landslides. For this purpose, the Buyukkoy catchment area, located in the East Black Sea Region in the east of Rize province and the south of Cayeli district, was selected as the study area. The investigations were executed in four different stages. These were (1) preparation of a temporal shallow landslide inventory of the study area, (2) assessment of conditioning factors in the catchment, (3) susceptibility analyses and (4) hazard evaluations and mapping. A total of 251 shallow landslides in the period of 1955–2007 were recognised using different data sources. A ‘Sampling Circle’ approach was proposed to define shallow landslide initiation in the mapping units in susceptibility evaluations. To accomplish the susceptibility analyses, the method of artificial neural networks was implemented. According to the performance analyses conducted using the training and testing datasets, the prediction and generalisation capacities of the models were found to be very high. To transform the susceptibility values into hazard rates, a new approach with a new equation was developed, taking into account the behaviour of the responsible triggering factor over time in the study area. In the proposed equation, the threshold value of the triggering factor and the recurrence interval are the independent variables. This unique property of the suggested equation allows the execution of more flexible and more dynamic hazard assessments. Finally, using the proposed technique, shallow landslide initiation hazard maps of the Buyukkoy catchment area for the return periods of 1, 2, 5, 10, 50 and 100 years were produced.

Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters

Abstract: Granitic rocks are commonly used as building and ornamental stones and pavement material in various civil engineering structures. However, the weathered material should not be used for these purposes. For this reason, determination of weathering degree of the granitic rocks is one of the important issues in rock engineering and engineering geology. In literature, it is possible to find some approaches for the determination of weathering degree of granitic rocks. Additionally, some soft computing methods have been used for the determination of the weathering degree of the granitic rocks. However, in literature, the non-linear multiple regression and the adaptive neuro-fuzzy inference system have not been used for the weathering classification yet. For this reason, the purpose of this study is to apply some statistical and soft computing methods such as artificial neural networks and adaptive neuro-fuzzy inference system on the determination of weathering degree of a granitic rock selected from Turkey by using some index and mechanical properties. The study includes four main stages such as sampling, testing, modeling and assessment of the model performances. During the modeling stage, three weathering prediction models with multi-inputs are developed with two soft computing techniques such as artificial neural networks and the adaptive neuro-fuzzy inference system, and a non-linear regression technique. The general performances of models developed in this study are close; however the adaptive neuro-fuzzy inference system exhibits the best performance considering the performance index and the degree of consistency. Finally, all models developed in the present study can be used when determining the weathering degree. However, the models developed in this study should be controlled by using the data at hand, before the use them in the practical purposes.

Probabilistic Risk Assessment in Medium Scale for Rainfall-Induced Earthflows: Catakli Catchment Area (Cayeli, Rize, Turkey)

Abstract: The aim of the present study is to introduce a probabilistic approach to determine the components of the risk evaluation for rainfall-induced earthflows in medium scale. The Catakli catchment area (Cayeli, Rize, Turkey) was selected as the application site of this study. The investigations were performed in four different stages: (i) evaluation of the conditioning factors, (ii) calculation of the probability of spatial occurrence, (iii) calculation of the probability of the temporal occurrence, and (iv) evaluation of the consequent risk. For the purpose, some basic concepts such as “Risk Cube”, “Risk Plane”, and “Risk Vector” were defined. Additionally, in order to assign the vulnerability to the terrain units being studied in medium scale, a new more robust and more objective equation was proposed. As a result, considering the concrete type of roads in the catchment area, the economic risks were estimated as 3.6×106€—in case the failures occur on the terrain units including element at risk, and 12.3×106€—in case the risks arise from surrounding terrain units. The risk assessments performed in medium scale considering the technique proposed in the present study will supply substantial economic contributions to the mitigation planning studies in the region.

Landform effect on rockfall and hazard mapping in Cappadocia (Turkey)

Abstract: The Cappadocia region has unique geomorphological features resulting from differential erosional processes which make it very attractive to tourists. Besides the fairy chimneys for which the area is best known, there are also impressive buttes and mesas. Buttes and mesas are formed in regions having flat-lying strata in which the uppermost levels are composed of well-cemented limestones and granular ignimbrites, whereas the lower parts and slopes consist of low-durability tuff and ignimbrites. This durability difference results in serious rockfall events. This study involves two-dimensional rockfall analyses in and near the Avanos, Zelve, and Cavusini areas, where volcano-sedimentary units of Neogene age outcrop, to provide a rockfall hazard map in which areas of tourism activity are also considered.

A complimentary fuzzy approach for the assessment of academic performance

Abstract: Assessment of academic performance is highly important and critical point for researchers, but this is a difficult task. The h-index is one of the most popular indicators to assess the academic performance. As the expression of academic performance is too difficult by single mathematical expression, it should be done with a mathematical expression to apply simply and safely throughout the world. However, this requirement does not change the nature of the problem. In fact, this is a real fuzzy problem and it should be tested by using fuzzy approaches. For this reason, construction of a fuzzy inference system to assess the academic performance is the main target of the current study. Although a close relationship between the h-index and the FAPI is obtained, there are still some slight differences. Obtained results show that problem is fuzzy, but its application is not very simple. For this reason, the fuzzy inference system should be considered as a complimentary tool for the assessment of academic performance, especially in carrier planning of the researchers.