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.
Seyed Masoud Soleimanpour; Bahram Hedayati; Majid Soufi; Mohammad Javad Rousta; Samad Shadfar
Abstract
One of the important relations in the erosion of gullies is to study the threshold of erosion creation and expansion. In recent decade, creation of new knowledge in determination of relation between variables was led to develop prediction methods in different science and therefore, investigating the ...
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One of the important relations in the erosion of gullies is to study the threshold of erosion creation and expansion. In recent decade, creation of new knowledge in determination of relation between variables was led to develop prediction methods in different science and therefore, investigating the ability to use these methods in erosion and soil conservation is essential. Also, in order to control the erosion of the gully, the mechanism of gullies growth and its dimension expansion, especially increasing in gullies length, has to be carefully determine; for this purpose, the present study aimed to determine the threshold of the most effective factors on increasing the length of the gully, using the K-Means data mining algorithms and the CART decision tree in the Ghazian watershed in the north of Fars province. The results of this study, which include measuring various variables of gullies under field condition and in laboratory, and using data mining techniques, showed that increasing the length of gully in this area depended on the factors of the area above headcut, saturated extract electrical conductivity, forehead slope, canopy cover percentage, and sodium adsorption ratio. It is recommended control of erosion in the foreheads is highly important in reducing the increase in gullies length and sediment production. Also, improving the soils of this area with soil amendments and the restoration of compatible vegetation and the increase in soil organic matter should be considered as the priority of effective actions to control the increasing length of gullies.
hamide afkhami; azam habibi pour; mohammad reza ekhtesasi
Abstract
Evaporation is considered one of the key climatic variables, especially in arid regions and evaporation losses is one of the important issues in irrigation and water resources management in these areas. Therefore, it is important being aware of the amount of evaporation and its modeling, as one of the ...
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Evaporation is considered one of the key climatic variables, especially in arid regions and evaporation losses is one of the important issues in irrigation and water resources management in these areas. Therefore, it is important being aware of the amount of evaporation and its modeling, as one of the most important hydrological variables in agricultural research and water and soil conservation. In recent decades, artificial intelligence techniques have proven high capability and flexibility to estimate and predict nonlinear phenomena. In this study, three important data mining techniques including Artificial Neural Network, Active Neuro-Fuzzy Inference System and Regression Decision Tree were used for predicting evaporation. For this purpose, 8 climatic variables (Minimum average temperature, average maximum temperature, average temperature, sunshine hours, wind speed, wind direction, relative humidity and evaporation averages) were employed in this study. The results showed three models are able to predict evaporation for 12 months after. Finally among the used models, ANN showed better performance with coefficient efficiency of 0.97 and RMSE of 5.1and ME of 0.48. Also, The results showed that there is not significant difference in simulation results to predict the evaporation between two scenario, original data and normalized data.
