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Erschienen in:
2016 | OriginalPaper | Buchkapitel
A Review on Artificial Intelligence Based Parameter Forecasting for Soil-Water Content
verfasst von : Ferhat Özçep, Eray Yıldırım, Okan Tezel, Metin Aşçı, Savaş Karabulut, Tazegül Özçep
Erschienen in: Machine Learning and Data Mining in Pattern Recognition
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The purpose of this study, by using an artificial intelligent approaches, is to compare a correlation between geophysical and geotechnical parameters. The input variables for this system are the electrical resistivity reading, the water content laboratory measurements. The output variable is water content of soils. In this study, our data sets are clustered into 120 training sets and 28 testing sets for constructing the fuzzy system and validating the ability of system prediction, respectively. Relationships between soil water content and electrical parameters were obtained by curvilinear models. The ranges of our samples are changed between 1 - 50 ohm.m (for resistivity) and 20 - 60 (%, for water content). An artificial intelligent system (artificial neural networks, Fuzzy logic applications, Mamdani and Sugeno approaches) are based on some comparisons about correlation between electrical resistivity and soil-water content, for Istanbul and Golcuk Soils in Turkey.