Abstract:This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited.
Predicting corporate bankruptcy has been an important challenging problem in research topic in accounting and finance. In bankruptcy prediction, researchers often confront a range of observations and variables which are often vast amount of financial ratios. By reducing variables and select relevant data from a given dataset, data reduction process can optimize bankruptcy prediction. This study addresses four well-known data reduction methods including t-test, correlation analysis, principal component analysis (PCA) and factor analysis (FA) and evaluated them in bankruptcy prediction in the Tehran Stock Exchange (TSE). To this end, considering 35 financial ratios, the results of data reduction methods were separately used to train Support Vector Machine (SVM) as the powerful prediction model. Regarding the empirical results, among the aforementioned methods, the t-test lead to the most prediction rate with 97.1% of predictability and PCA by 95.1% provides the next position.
Predicting corporate bankruptcy has been an important challenging problem in research topic in accounting and finance. In bankruptcy prediction, researchers often confront a range of observations and variables which are often vast amount of financial ratios. By reducing variables and select relevant data from a given dataset, data reduction process can optimize bankruptcy prediction. This study addresses four well-known data reduction methods including t-test, correlation analysis, principal component analysis (PCA) and factor analysis (FA) and evaluated them in bankruptcy prediction in the Tehran Stock Exchange (TSE). To this end, considering 35 financial ratios, the results of data reduction methods were separately used to train Support Vector Machine (SVM) as the powerful prediction model. Regarding the empirical results, among the aforementioned methods, the t-test lead to the most prediction rate with 97.1% of predictability and PCA by 95.1% provides the next position.
Today’s data driven world exploiting the latest trends of database and its allied technologies like Data Warehouse and Data Mining. Data Mining in recent years emerged as one of the most efficient database technique proved to be very reliable almost in every organisation enabling to find previously unknown hidden data patterns for the benefit of organisation. At the same time it is imposing serious problems concerned to data privacy and its potential misuse.
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