Milad Momtazi Burojeni; Fereydoon Sarmadian
Abstract
Soil resource management is essential to maintain community production and the environment. Soil is usually used to produce agricultural products and livestock fodder. As a result, the mapping of high-resolution digital maps is crucial for the distribution of soil and soil properties and land management. ...
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Soil resource management is essential to maintain community production and the environment. Soil is usually used to produce agricultural products and livestock fodder. As a result, the mapping of high-resolution digital maps is crucial for the distribution of soil and soil properties and land management. The decision tree model is a widely used method for predicting soil class in digital soil mapping studies. This study aimed to provide a digital soil mapping in four levels of taxonomy using a decision tree with Boost-reinforced C5.0 algorithm using satellite data and digital Elevation Model and geological maps as environmental variables in 41,000 hectares of Abyek Area. This area was identified using randomized gridding of the geographic location of 128 soil profiles and then described, sampled, and classified. In this research, using the principal component analysis method on environmental variables, 20 environmental variables were selected as the representative of stacking factors for modeling. Multiresolution Valley Flatness Index is the most important environmental variable that was selected as input for the model. The results of the overall accuracy of the integrated model for predicting taxonomic levels of the Order, Suborder, great group, and subgroup were shown to be 89%, 85%, 58%, and 58%, respectively. The study also examined the effect of the boosting technique on the tree model, which showed that all taxonomic levels were better predicted by using the boost model than when no boosting was used and boosting resulted in an increase in overall accuracy and kappa coefficient It turned out.
Toktam Imani; Mahdi Delghandi; Samad Emamgholizadeh; Zahra Ganji Noroozi
Abstract
Flood hazard assessment is an important topic that can reduce flood-related losses. Rainfall-runoff modeling plays a key role in the management of water resources in addition to protecting from flood hazards. The use of hydrological models to simulate the runoff necessitates the proper calibration of ...
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Flood hazard assessment is an important topic that can reduce flood-related losses. Rainfall-runoff modeling plays a key role in the management of water resources in addition to protecting from flood hazards. The use of hydrological models to simulate the runoff necessitates the proper calibration of the different parameters. Therefore, In the present study, the Watershed Modeling System (WMS11.0) was evaluated to simulate peak discharge and volume of floods of Babolrood catchment. WMS model calibrated and validated using 3 and 2 rainfall events, respectively. Afterwards, design precipitation (DP) for 2, 5, 10, 25, 50, 100 and 500-year return periods was determined and flood resulting from DPs simulated. The results showed that the WMS model could accurately estimate the peak discharge (the error was about 5%) and the and flood volume (the error was less than 26%). But the model was not able to simulate properly the shape of the hydrograph. It also revealed that peak discharge and flood volume arising from 2 to 500-year return periods of rainfall vary between 50 to 300 m3/s and 6.6 to 32.4 Mm3, respectively.
vahid chitsaz; Aliakbar Nazari Samani; Saeed Soltani; sadat feiznia
Abstract
Sediment and erosion are two natural phenomena in watersheds. Due to irregular recording and sampling difficulties, daily data are not available for sediment records. Therefore, decision makers and researchers have to apply interpolating methods to estimated sediment yields. In this study, 30 watershed ...
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Sediment and erosion are two natural phenomena in watersheds. Due to irregular recording and sampling difficulties, daily data are not available for sediment records. Therefore, decision makers and researchers have to apply interpolating methods to estimated sediment yields. In this study, 30 watershed characteristics including physiography, geomorphology, vegetation, climate conditions in 69 watersheds located in the Karoon and Karkheh basins were used to statistical analysis. Based on the principle component analysis, eight characteristics including area, perimeter, river length, relief, mean of elevation at 85% upstream and 15% point of longest flow path and the number of landslide events were selected. Then using Cluster Analysis, six homogenous regions were identified and multiple regression models were applied. Due to constriction of large dames on the studied watersheds, access to the reliable data is a challenges for sediment yield analysis. Based on the sediment-precipitation double-mass curves 29 out of 35 stations were influenced by upstream dam. Results indicated that the effects of large reservoir dams can influence the downstream sediment yield along 98 Km of river length. The results show that in each group a particular combination of variables influence the sediment yields of the watersheds. According to the validation indices (NS and R2) the obtained models have the high performance (R2 = 0.71 and NS=0.72). In general, the physiographic characteristics of the watershed such as length, area, main flow path and relief are more important than other climatic, vegetation and geological factors. The total explain variance by the mentioned variables is 87.3%.
