Showing 2 results for Cross Validation
B. Saghafian, S. Rahimi Bandarabadi, H. Taheri Shahraeeni and J. Ghayoomian,
Volume 24, Issue 1 (7-2005)
Abstract
Rainfall is one of the most important climatic variables in the hydrology cycle. In flood estimation as well as environmental pollution studies in medium to large watersheds not only mus temporal pattern of rainfall t be known, but also the knowledge of its spatial distribution is required. Estimation of daily rainfall distribution without comparison and selection of
suitable methods may lead to errors in input parameters of rainfall – runoff models. Interpolation methods are among the techniques for estimating spatial distribution of rainfall. In this study, Thin Plate Smoothing Splines (TPSS), Weighted Moving Average (WMA) and Kriging are applied to estimate spatial daily rainfall in the southwest of Iran. Cross validation technique is used for comparison and evaluation of the methods. The results of analysis with two different station density showed that the TPSS method with power of 2 is the most accurate method in estimating daily rainfall. Zoning of the region also increased the interpolation accuracy. Generally speaking, division of the region based on cluster analysis improves accuracy compared with division by inter basin boundaries
A.a. Gharehaghaji, M. Palhang, and M. Shanbeh,
Volume 24, Issue 2 (1-2006)
Abstract
Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile
properties of cotton-covered nylon core yarns. Multilayer Feedforward network with Back Propagation learning algorithm was used to
study the relationship and mapping among the process parameters, i.e. count of sheath part, count of core part, applying pretension to the core part, inserted twist to the core spun-yarn as well as tensile properties, i.e. breaking strength and breaking elongation. The results show that ANN is an effective method for the prediction of the tensile properties of these yarns. This is due to the fact that in each case, standard deviation of prediction error for test and train data was less than that obtained from the expreiments.
