Civil Engineering / İnşaat Mühendisliği

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

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Author: search.filters.author.Aksoy, HafzullahScopus Q: search.filters.scopusQuartile.Q2

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 6
    Prediction of Rainfall Runoff-Induced Sediment Load From Bare Land Surfaces by Generalized Regression Neural Network and Empirical Model
    (Wiley, 2020) Tayfur, Gökmen; Aksoy, Hafzullah; Eriş, Ebru
    Based on three rainfall run-off-induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. The GRNN model was trained and tested. The GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. In addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. The empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. The validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. The results indicated the success of the empirical models in predicting sediment load from bare land surfaces.
  • Article
    Citation - WoS: 18
    Citation - Scopus: 24
    Empirical Sediment Transport Models Based on Indoor Rainfall Simulator and Erosion Flume Experimental Data
    (John Wiley and Sons Inc., 2017) Aksoy, Hafzullah; Eriş, Ebru; Tayfur, Gökmen
    Land degradation processes start with accelerated runoff and sediment delivery. In this study, rainfall-runoff induced sediment transport is investigated using data from an indoor laboratory experimental setup consisting of a rainfall simulator and an erosion flume. The data are analysed to develop empirical models using sediment discharge, slope, flow discharge, rainfall intensity and sediment size. Fine and medium sands are considered as bare soil in experiments. Four rainfall intensities (45, 65, 85 and 105 mm h−1) are applied with combinations of lateral and longitudinal slopes of 5%, 10%, 15% and 20%. Eighty experiments are conducted. Flow is measured, and sediment within flow is separated and weighted. Experimental data are used for developing empirical models through multiple regression with parameters optimized by genetic algorithm. Results show that slope is the main contributing variable to the sediment transport over hillslopes. Accommodating variables among slope, rainfall intensity, flow discharge and median diameter of sediment as independent variables, one-variable, two-variable and four-variable models are developed considering also that higher number of parameters increases the performance of the model with higher cost of parameterization.