A. Rezaei, M. Mahdavi, K. Luxe, S. Feiznia, M. H. Mahdian,
Volume 11, Issue 1 (4-2007)
Abstract
The model in this research was created based on the Artificial Neural Network (ANN) and calibrated in the Sefid-rood dam basin (excluding Khazar zone). This research was done by gathering and selecting peak flows of hydrographs from 12 sub basins, the concentration time of which was equal to or less than 24 hours and was caused only by rainfall. From all the selected sub basins, totally 661 hydrographs were prepared and their peak flows data wes used to make prediction model. The input variables of the model consisted of the depth of daily flooding rainfalls, and so the five days before rainfall of every peak flow, the area of sub basins, the main stream length, the slope of 10-85 percent of main stream, the median height of sub basins, the area of geological formations and rock units, classified at three hydrological groups of I, II, III, the base flow, and output variable was only peak flow. By using Feed Forward Artificial Neural Network with training method of back propagation error the function approximation of inputs to output was created by passing the three processes of training (learning), testing and validation. So based on that data and variables, the Multivariable Linear Regression model was created. The comparison of observed peak flows, based on validation data package, showed that the statistical parameters of (R2) coefficient and Fisher’s test parameter coefficient (F) for ANN model and MLR respectively were 0.84, 33.66 and 0.33, 3.60, indicating the superiority of ANN to traditional methods.
A. R. Vaezi, Sh. Karimi, M. Foroumadi,
Volume 23, Issue 4 (12-2019)
Abstract
Rainfall erosion is the first type of water erosion on the land which is affected by various factors such as land use change and previous rainfall. This study was carried out to investigate the influence of previous rainfalls on the process of rainfall erosion in two marl soils (pasture and that changed to agriculture) under the simulated rainfall. Toward this goal, aggregate samples with the diameters of 6 to 8 mm were randomly collected from the marl areas in the west of Zanjan. Soil aggregates were packed into 48 boxes with the dimension of 30×40 cm to examine the effects of eight rainfall durations with three replications. Eight simulated rainfalls with the duration of 0, 7, 14, 21, 28, 35, 42 and 49 min and a constant intensity of 40 mm h-1 were used in the experiment. The soils were exposed to another simulated rainfall with 40 mm h-1 in terms of intensity for 15 min to study the rainfall erosion processes. The results showed that the aggregate breakdown was significantly affected by the previous rainfalls in the pasture soil (P<0.01), while there was no significant difference among the previous rainfalls in the case of agriculture soil. Soil compaction and particles splash were significantly affected by previous rainfalls (P<0.05). Aggregate breakdown and particles splash were 1.41 and 1.31 times bigger than their values in the pasture soil. This study, therefore, revealed that the land use change in the mal areas increases the soil vulnerability to rainfall erosion processes. The rate of rainfall erosion processes in each rainfall event depends on the amount of previous rainfall. Increasing aggregate break down and soil water content by the previous rainfall could significantly influence the splash erosion rate in a marl soil.
