Data Types and Logical Processing Methods

Abstract

Not only numerical but also linguistic data are necessary in the modeling of earth sciences events. Measurements are sources of numerical data whereas observations lead to linguistic data. Numerical data include randomness and errors but linguistic data are rather fuzzy, which means that there are uncertainties in both data types. Accordingly, the final model results as predictions or estimations include errors that must be confined within ±5% limits in practical applications. Spatial estimations can be obtained either on point basis or on sub-areal basis depending on the refinement of the problem at hand and purpose. In general, longitude (easting), latitude (northing), and regionalized variable (ReV) value at this location are necessary for a complete description and establishment of a point-wise spatial model, where these three values are referred to as triplicate; but in the case of pixel location its size is also necessary, which leads to four variables (quadruple) for the description of ReV. Simple classical triangularization, polygonalization techniques are used in addition to innovative percentage polygon methodology. Droughts are a kind of spatial earth sciences with coverage area that can be modeled by probabilistic approaches.