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.
Gh. A. Fallah Ghalhary; M. Mousavi Baygi; M. Habibi Nokhandan; J. Khoshhal
Volume 62, Issue 1 , June 2009, , Pages 111-124
Abstract
The research show that global climate changes and atmospheric general circulation are affected by large scale phenomena that occurred in the sea surface. These large scale phenomena are often named "climate large scale signals". These signals are calculated based on criteria such as sea Level Pressure ...
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The research show that global climate changes and atmospheric general circulation are affected by large scale phenomena that occurred in the sea surface. These large scale phenomena are often named "climate large scale signals". These signals are calculated based on criteria such as sea Level Pressure (SLP), Sea Surface Temperature (SST) and so on. A method for weather forecasting is a special approach based on statistical modeling. In this study, data of 37 rainfall stations were used to model the relation between precipitation and Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Level Pressure gradient (?SLP) and the difference between sea surface temperature and air temperature at 1000 HP. The results show that statistical modeling can successfully predict the amount of annual rainfall. The mean root square error for stepwise model were obtained 49 millimeters.