Ebru Akcapinar Sezer

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

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.

Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia)

This paper presents the results of the neuro-fuzzy model using remote-sensing data and geographic information system for landslide susceptibility analysis in a part of the Cameron Highlands areas in Malaysia. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map the vegetation index. Maps of the topography, lineaments, Normalized Difference Vegetation Index (NDVI), and land cover were constructed from the spatial data sets. Eight landslide conditioning factors such as altitude, slope gradient, curvature, distance from the drainage, distance from the road, lithology, distance from the faults, and NDVI were extracted from the spatial database. These factors were analyzed using a neuro-fuzzy model adaptive neuro-fuzzy inference system to produce the landslide susceptibility maps. During the model development works, a total of five landslide susceptibility models were constructed. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all landslide susceptibility models were drawn, and the area under curve values were calculated. Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed a 97% accuracy for model 5, employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed a sufficient agreement between the obtained susceptibility map and the existing data on the landslide areas. Qualitatively, the model yields reasonable results, which can be used for preliminary land-use planning purposes.

Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey

The main purpose of the present study is to investigate the possible application of decision tree in landslide susceptibility assessment. The study area having a surface area of 174.8 km2 locates at the northern coast of the Sea of Marmara and western part of Istanbul metropolitan area. When applying data mining and extracting decision tree, geological formations, altitude, slope, plan curvature, profile curvature, heat load and stream power index parameters are taken into consideration as landslide conditioning factors. Using the predicted values, the landslide susceptibility map of the study area is produced. The AUC value of the produced landslide susceptibility map has been obtained as 89.6%. According to the results of the AUC evaluation, the produced map has exhibited a good enough performance.

Application of two non-linear prediction tools to the estimation of tunnel boring machine performance

Predicting tunnel boring machine (TBM) performance is a crucial issue for the accomplishment of a mechanical tunnel project, excavating via full face tunneling machine. Many models and equations have previously been introduced to estimate TBM performance based on properties of both rock and machine employing various statistical analysis techniques. However, considering the nature of the problem, it is relatively difficult to estimate tunnel boring machine performance by linear prediction models. Artificial neural networks (ANNs) and non-linear multiple regression models have great potential for establishing such prediction models. The purpose of the present study is the construction of non-linear multivariable prediction models to estimate TBM performance as a function of rock properties. For this purpose, rock properties and machine data were collected from recently completed TBM tunnel project in the City of New York, USA and consequently the database was established to develop performance prediction models utilizing the ANN and the non-linear multiple regression methods. This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations.

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

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.

A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments

The Analytical Hierarchy Process (AHP) is a classic and powerful decision support tool. However, the conventional AHP has some disadvantages originating in the expert decision-making process. To minimize the disadvantages of the conventional AHP, a modified analytical hierarchy process (M-AHP), is suggested in this study. This study is conducted in three stages: (i) the theoretical background for the conventional AHP is introduced, (ii) essentials for the proposed M-AHP technique are given with an example solution for the evaluation of snow avalanche source susceptibility, and (iii) a computer code named M-AHP is presented. By applying the methodology suggested in this study, the consistency ratio value for the comparison matrix and the weight vector never exceeds 0.10. The M-AHP program is a complementary tool for natural hazard, natural resource, or nature preservation researchers who apply the M-AHP technique to their decision support problem.

An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents

The uniaxial compressive strength (UCS) of rocks is an important intact rock parameter, and it is commonly used for various engineering applications. This parameter is mainly controlled by the mineralogical and textural characteristics of rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of rocks.

A comparison of some soft computing methods for software fault prediction

Software fault prediction is implemented with ANN, SVM and ANFIS.First ANFIS implementation is applied to solve fault prediction problem.Parameters are discussed in neuro fuzzy approach.Experiments show that the application of ANFIS to the software fault prediction problem is highly reasonable. The main expectation from reliable software is the minimization of the number of failures that occur when the program runs. Determining whether software modules are prone to fault is important because doing so assists in identifying modules that require refactoring or detailed testing. Software fault prediction is a discipline that predicts the fault proneness of future modules by using essential prediction metrics and historical fault data. This study presents the first application of the Adaptive Neuro Fuzzy Inference System (ANFIS) for the software fault prediction problem. Moreover, Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods, which were experienced previously, are built to discuss the performance of ANFIS. Data used in this study are collected from the PROMISE Software Engineering Repository, and McCabe metrics are selected because they comprehensively address the programming effort. ROC-AUC is used as a performance measure. The results achieved were 0.7795, 0.8685, and 0.8573 for the SVM, ANN and ANFIS methods, respectively.

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

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.

An assessment on producing synthetic samples by fuzzy C-means for limited number of data in prediction models

Use of synthetic data in indirect determination of rock strength was investigated.Fuzzy C-means is a practical and suitable method for synthetic data production.Prediction models can be implemented by using 40-50% of measured data. For most of rock engineering and engineering geology projects, strength and deformability parameters of intact rocks have crucial importance. However, it is highly challenging to obtain these parameters from weak and very weak rocks due to their nature and testing requirements. For this reason, prediction models are commonly used to obtain desired parameters indirectly. When developing a prediction model, data sets having sufficient size are required. If sufficient data size is not provided for a prediction model, insufficient data problem arises. The main purpose of this study was to investigate the use of synthetic data in indirect determination of rock strength by employing fuzzy C-means (FCM) and adaptive neuro-fuzzy inference system (ANFIS). For the purpose, the experiments were carried out in two stages; (i) uniaxial compressive strength (UCS) prediction by using real data with ANFIS, and (ii) production of synthetic data sets having different sizes, and synthetic data set evaluation in modeling. According to the results obtained, FCM is a practical and suitable method for synthetic data production. Development of prediction models for rock strength by using synthetic data is found to be successful based on statistical performance indices. Additionally, the use of proposed size for synthetic data reduces modeling effort significantly because it eliminates the iterative approach in modeling, hence development of models for limited number of data becomes more practical.