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Öğe Evaluation of Mn concentration provided by soil in citrus-growing regions(Elsevier Sci Ltd, 2009) Tutmez, B.; Dag, A.; Erdem, H.; Torun, B.Manganese (Mn) is an essential nutrient element in citrus growing and Mn deficiency causes some problems related with physiological and morphological structure. Spatial evaluation of Mn obtained from soils in citrus-growing areas is the main objective of this paper. For this purpose, a citrus-growing region in Turkey has been selected and three effective estimation methods: kriging, neural-fuzzy modelling, and fuzzy interval arithmetic have been considered for the spatial evaluations. The model works primarily focus on the model accuracy and smoothing degree of estimations. In addition, error analysis and comparative assessments, which present the advantages and drawbacks of the models, are conducted in the paper. The results and performance evaluations prove the superiorities of soft computing approach in this evaluation. (C) 2009 Elsevier B.V. All rights reserved.Öğe Lignite thickness estimation via adaptive fuzzy-neural network(Uceat-Chamber Mining Engineers Turnkey, 2007) Tuetmez, B.; Dag, A.; Tercan, A. E.; Kaymak, U.Thickness estimation is an important step in reserve estimation. In this study, lignite thickness is estimated using fuzzy-neural network. For this purpose, the lignite thickness data derived from Afsin-Elbistan lignite deposit were employed and the estimation has been conducted by the Adaptive Network Based Fuzzy Inference System (ANFIS). The method estimates thickness based on a data-driven model structure which is constructed from the adaptation of artificial neural networks to fuzzy modelling algorithm. Modelling process consists of data clustering, inference and learning mechanisms. The results have been compared with kriging estimations and it is seen that performance of the model is high.Öğe A linguistic model for evaluating cement strength(Springer, 2009) Tutmez, B.; Dag, A.This paper presents a soft methodology for predicting the 28-day compressive strength of Portland cement (CCS) by making use of the 1-day, 3-day and 7-day CCS values. Data taken from a cement plant in Turkey have been employed in the model construction and testing. For implementation, linguistic models were designed based on if-then fuzzy rules. In addition, predictions of these models were compared with results of the regression models. The performance evaluations showed that the linguistic-based fuzzy predictions are very satisfactory in estimating cement strength and the linguistic modeling performs better than regression modeling.Öğe Measuring Dependence between Calorific Values of Lignite and Spatial Positions by Rank Correlation Method: A Case Study(Taylor & Francis Inc, 2010) Tutmez, B.; Dag, A.; Cengiz, A. K.This article presents an investigation for evaluating the relationship between calorific value of lignite and its spatial position using non parametric statistics. Measuring dependence between calorific values and spatial positions is important for production planning and making decisions. For this purpose, a three dimensional data set (easting-x, northing-y, and elevation-z) obtained from Afsin-Elbistan lignite deposit is employed. Rank correlation measurements have been carried out between spatial dimensions and calorific values. In addition, the significances of rank correlation coefficients have been appraised using hypothesis tests. As a result of the study, a strong rank correlation between y and calorific values and a weak rank correlation between z and calorific values are observed, respectively. On the other hand, very low-rank correlation between coordinate x and calorific values is measured for the deposit.
