A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while one-third of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.     

Geochemistry of Archean Metavolcanic Rocks from Kadiri Schist Belt, Andhra Pradesh, India

The basic volcanic group exposed in the Kadiri schist belt includes high Mg-basalt, basalt, basaltic andestite and dacite. The basalts are tholeiitic in composition and high Mg-basalts, basaltic andesites and dacites show calc-alkaline affinity. Major and trace element characteristics suggest that the volcanic suite has been derived from an initial tholeiitic magma which has given rise to an early basaltic type and a later calc-alkaline type of rocks. An island arc and active continental margin tectonic setting was inferred for these rocks.     

Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping

This study compares the predictive performance of GIS-based landslide susceptibility mapping (LSM) using four different kernel functions in support vector machines (SVMs). Nine possible causal criteria were considered based on earlier similar studies for an area in the eastern part of the Khuzestan province of southern Iran. Different models and the resulting landslide susceptibility maps were created using information on known landslide events from a landslide inventory dataset. The models were trained using landslide inventory dataset. A two-step accuracy assessment was implemented to validate the results and to compare the capability of each function. The radial basis function was identified as the most efficient kernel function for LSM with the resulting landslide susceptibility map showing the highest predictive accuracy, followed by the polynomial kernel function. According to the obtained results, it concluded that using SVMs can generally be considered to be an effective method for LSM while it demands careful consideration of kernel function. The results of the present research will also assist other researchers to select the best SVM kernel function to use for LSM.     

Model Pile Groups Under Oblique Pullout Loads – an Investigation

Experimental investigations on model pile groups of configuration, 1 × 1(single pile), 3 × 1, 2 × 2 for embedment length to diameter ratio, L/d = 38, were conducted in uniform dry medium dense Ennore sand. The spacing of piles in the groups varied from 3 to 6 pile diameter. Soil–pile friction angles were δ = 16° and 28°. The pile groups were subjected to oblique pulling loads at angles θ = 0°, 30°, 60° and 90° with the vertical central axis of the groups. The load–displacement response, oblique ultimate pulling resistances have been qualitatively and quantitatively studied. The inclinations of the load, at which maximum oblique resistance for the groups were observed, have been reported. Predictions of ultimate resistance of pile groups under uplift, lateral and oblique pulling loads have been carried out respectively by the methods of Patra and Pise (2002) (Electronic Journal of Geotechnical Engineering, 8, Bundle B), Patra and Pise (2001) (Journal of Geotechnical and Geoenvironmental Engineering ASCE, 127(6), 481–487) and Chattopadhyay and Pise (1986c) (Proceedings of IST East Asian Conference on Struct Engineering and Const., Vol. 1, pp. 1632–1641). A comparison of the measured values of the Writers and others with the predicted values showed reasonable agreement.     

Unsteady curvilineal flow of an incompressible simple fluid between two rotating concentric spheres

Summary Unsteady curvilineal flow of an incompressible simple fluid (based on Noll's theory) representing the flow between two rotating concentric spheres is considered. The outer sphere is assumed to undergo forced harmonic oscillations about a vertical axis through its centre, while the inner sphere is constrained by a torsionwire. It is found that exact solution in terms of Bessels functions exists under certain conditions on the shear stress function and the normal stress functions.