Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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COAR Access Rights: search.filters.coarAccessRights.open accessSubject: search.filters.subject.Hydraulic conductivity

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  • Article
    Citation - WoS: 43
    Citation - Scopus: 49
    Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation
    (Springer Verlag, 2014) Tayfur, Gökmen; Nadiri, Ata A.; Moghaddam, Asghar A.
    Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.
  • Article
    Citation - WoS: 12
    Citation - Scopus: 13
    Modeling Deficit Irrigation in Alfalfa Production
    (American Society of Civil Engineers (ASCE), 1995) Tayfur, Gökmen; Tanji, Kenneth K.; House, Brett; Robinson, Frank; Teuber, Larry; Kruse, Gordon
    A conceptual agronomic model EPIC was extended to consider the effects of salinity in alfalfa production under optimal and water stress irrigation conditions. The extended model was calibrated and validated with observed lysimeter data. The model parameters that affected alfalfa yield and soil salinity the most were wilting point, field capacity, hydraulic conductivity, nitrate concentration, biomass energy ratio, seeding rate, average soil salinity EC e at which crop yield is reduced by 50% ( EC50 ), and initial soil gypsum concentration. The calibrated and validated model was then applied to an alfalfa deficit irrigation study. The four irrigation treatments included optimum check, minimum stress, short stress, and long stress, each of which produced differential alfalfa yields. The purpose of summer deficit irrigation was to ascertain how much agricultural water at what cost could be made available for urban water uses during water shortfalls. The results of model simulation were found to be satisfactory under all irrigation treatments though the model slightly overestimated the yields and underestimated the soil EC e at the end of short and long stress treatments. An economic component is included to determine the appropriate compensation for farmers undergoing a range of deficit irrigations.