Abstract: This study investigates the use of a time-series of
MODIS NDVI data to identify agricultural land cover change on an
annual time step (2007 - 2012) and characterize the trend. Following
an ISODATA classification of the MODIS imagery to selectively
mask areas not agriculture or semi-natural, NDVI signatures were
created to identify areas cereals and vineyards with the aid of
ancillary, pictometry and field sample data for 2010. The NDVI
signature curve and training samples were used to create a decision
tree model in WEKA 3.6.9 using decision tree classifier (J48)
algorithm; Model 1 including ISODATA classification and Model 2
not. These two models were then used to classify all data for the
study area for 2010, producing land cover maps with classification
accuracies of 77% and 80% for Model 1 and 2 respectively. Model 2
was subsequently used to create land cover classification and change
detection maps for all other years. Subtle changes and areas of
consistency (unchanged) were observed in the agricultural classes
and crop practices. Over the years as predicted by the land cover
classification. Forty one percent of the catchment comprised of
cereals with 35% possibly following a crop rotation system.
Vineyards largely remained constant with only one percent
conversion to vineyard from other land cover classes.
Abstract: This paper deals with econometric analysis of real
retail trade turnover. It is a part of an extensive scientific research
about modern trends in Croatian national economy. At the end of the
period of transition economy, Croatia confronts with challenges and
problems of high consumption society. In such environment as
crucial economic variables: real retail trade turnover, average
monthly real wages and household loans are chosen for consequence
analysis. For the purpose of complete procedure of multiple
econometric analysis data base adjustment has been provided.
Namely, it has been necessary to deflate original national statistics
data of retail trade turnover using consumer price indices, as well as
provide process of seasonally adjustment of its contemporary
behavior. In model establishment it has been necessary to involve the
overcoming procedure for the autocorrelation and colinearity
problems. Moreover, for case of time-series shift a specific
appropriate econometric instrument has been applied. It would be
emphasize that the whole methodology procedure is based on the real
Croatian national economy time-series.
Abstract: In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.