Prediction of rainfall runoff-induced sediment load from bare land surfaces by generalized regression neural network and empirical model

Tayfur G., Aksoy H., Eris E.

WATER AND ENVIRONMENT JOURNAL, vol.34, no.1, pp.66-76, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.1111/wej.12442
  • Page Numbers: pp.66-76
  • Keywords: bare slope, empirical model, genetic algorithm, GRNN, sediment load, SOIL-EROSION, TRANSPORT CAPACITY, OVERLAND-FLOW, STEEP SLOPES, GIS, SIMULATION, DISCHARGE, DESIGN, RILL


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.