Classification accuracy depends on a number of factors, of which the nature of the training samples, the number of bands used, the number of classes to be identified relative to the spatial resolution of the image and the properties of... more
Classification accuracy depends on a number of factors, of which the nature of the training samples, the number of bands used, the number of classes to be identified relative to the spatial resolution of the image and the properties of the classifier are the most important. This paper evaluates the effects of these factors on classification accuracy using a test area in La Mancha, Spain. High spectral and spatial resolution DAIS data were used to compare the performance of four classification procedures (maximum likelihood, neural network, ...
An artificial neural network (ANN) and support vector machines (SVMs) were employed for estimating the removal efficiency of settling basins in canals. The performance of ANN and SVMs was tested using the data from an earlier study... more
An artificial neural network (ANN) and support vector machines (SVMs) were employed for estimating the removal efficiency of settling basins in canals. The performance of ANN and SVMs was tested using the data from an earlier study carried out by Ranga Raju et al. As compared with the Ranga Raju et al. relationship, the correlation coefficient of ANN as well as SVMs improved from 0.77 to 0.9854 and 0.9853, respectively; whereas the root mean squared error values decreased from 50.66 to 5.712 and 5.7366, respectively. Between ...
This paper presents the results of an application of a support vector machine based modelling technique to determine the end-depth ratio and discharge of a free overfall occurring over an inverted smooth semi-circular channel and a... more
This paper presents the results of an application of a support vector machine based modelling technique to determine the end-depth ratio and discharge of a free overfall occurring over an inverted smooth semi-circular channel and a circular channel with flat bases. The data used in this study are taken from earlier studies reported in the literature. The results of the study indicate that the support vector machine technique can be used effectively for predicting the end-depth ratio and the discharge for such channels. For ...
This paper explores the potential of an artificial immune‐based supervised classification algorithm for land‐cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear... more
This paper explores the potential of an artificial immune‐based supervised classification algorithm for land‐cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear classification, self/non‐self identification, and negative selection. Landsat ETM+ data of an area lying in Eastern England near the town of Littleport are used to study the performance of the artificial immune‐based classifier. A univariate decision tree and maximum likelihood classifier were used ...
This note investigates the potential of support vector machines based regression approach to model the static pile capacity from dynamic stress-wave data. A data set of 105 prestressed precast high strength concrete spun pipe piles is... more
This note investigates the potential of support vector machines based regression approach to model the static pile capacity from dynamic stress-wave data. A data set of 105 prestressed precast high strength concrete spun pipe piles is used. Radial basis function and polynomial kernel based support vector machines were used to model the total pile capacity and results were compared with a generalized regression neural network approach. A total of 81 data set were used to train, whereas the remaining 24 data sets ...
