Mahshid Souri; mirfarhad blurfrush; Hirad Aghbari; javad motamedi; Behnaz Attaeian
Volume 27, Issue 3 , October 2020, , Pages 369-409
Abstract
If the rangeland forage is used continuously, the important elements such as NPC do not return to the soil, which will cause the rangeland lands to lose their fertility. Therefore, nowadays, in the field of rangeland management, rangeland improvement and rehabilitation has become ...
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If the rangeland forage is used continuously, the important elements such as NPC do not return to the soil, which will cause the rangeland lands to lose their fertility. Therefore, nowadays, in the field of rangeland management, rangeland improvement and rehabilitation has become very important. The use of fertilizers is one of the methods to rehabilitation the rangelands. If the proper fertilizer application is carried out in accordance with the climatic conditions, cover condition, and soil characteristics, it will improve the rangeland. Otherwise, it will increase the concentration of salts, soil toxicity, and surface and groundwater contamination and leads to drying of the plants. The aim of this study is to present a model based on the use of an artificial neural network that expresses the relationships between organic carbon, nitrogen and phosphorus of rangeland soil and plant factors, based on which, it is possible to estimate the mentioned elements in the rangeland ecosystems without statistics to manage fertilization. Based on the results, organic carbon, nitrogen and phosphorus of the soil were estimated in the Nazlocha rangeland of Urmia. Eight factors of electrical conductivity, acidity, clay percentage, silt percentage, sand percentage, lime content, production and canopy cover percentage of rangeland plants were also selected for factor analysis. Therefore, according to the mentioned results, the neural network was able to accurately predict the amount of organic carbon, nitrogen and phosphorus in rangeland soils. According to the results, the vegetation type Astragalus gummifera-prangos uloptera-Bromus tomentellus requires phosphorus and nitrogen fertilizers. Onobrychis cornuta- Festuca ovina-Thymus kotschyanus requires phosphorus fertilizer, and Astragalus macrostachys-Noeae mucronata-Stipa barbata requires organic matter and nitrogen and phosphorus fertilizers.
Bahareh Jebalbarezi; Arash Malekian
Volume 26, Issue 2 , July 2019, , Pages 292-301
Abstract
Modeling and prediction of groundwater level is one of the basic tasks to achieve optimal management of water resources. One way to predict the groundwater level is using artificial intelligence techniques such as neural networks and gene expression planning. The aim of this study ...
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Modeling and prediction of groundwater level is one of the basic tasks to achieve optimal management of water resources. One way to predict the groundwater level is using artificial intelligence techniques such as neural networks and gene expression planning. The aim of this study was to evaluate the effectiveness of artificial neural network (ANN) and gene expression methods in predicting groundwater level of Jiroft plain aquifer. For this purpose, the data from 65 piezometric wells in the Jiroft plain aquifer was used for a period of eleven years. The level of piezometric wells by each of the techniques of gene expression and neural network were simulated separately and at the end, the root mean square, mean absolute error, and R2 were used to determine the accuracy of the predictions of each of the methods.The results of this study showed the higher efficiency and accuracy of both neural network techniques and gene expression in predicting the groundwater level region. The correlation coefficient in the artificial neural network method gene expression method was equal to 0.96 and 0.72, respectively, indicating the higher efficiency of artificial neural network in the simulation of Jiroft plain groundwater data over the period studied.
Mehdi Tazeh; Maryam Asadi; Rouhollah Taghizadeh Mehrjerdi; Saeedeh Kalantari; Majid Sadeghinia
Volume 25, Issue 1 , April 2018, , Pages 29-43
Abstract
Geomorphological map is one of the main information layers in natural resources studies. So far, various methods have been proposed for the classification and separation of various units and Geomorphological types, most of which are based on qualitative and descriptive information. In this study, the ...
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Geomorphological map is one of the main information layers in natural resources studies. So far, various methods have been proposed for the classification and separation of various units and Geomorphological types, most of which are based on qualitative and descriptive information. In this study, the ability of geomorphometry parameters in separation of mountains from pediment and also separation of different types of pediments was investigated. First, ground truth map was prepared using visual interpretation of satellite data and topographic maps. Then the 1000-point sampling grid was designed randomly. Parameters including profile curvature, plan curvature, tangential curvature, cross-sectional curvature, longitudinal curvature, and general curvature were prepared from digital elevation model in the GIS software. Then, their values were extracted at all points of the sampling network. Then, artificial neural network with structure of 13_6_ 4 was used to separate the units. The results showed that the erosion pediment could be separated from epandage using artificial neural network; however, the separation of epandage pediment from covered pediment was not well. For this purpose, to improve network performance, the digital value of Landsat 7 data was added to the previous values. The resolution accuracy of mountain, erosion pediment, epandage pediment, and covered pediment was calculated to be 90, 79, 80, and 76%, respectively.
Leila Khalasi Ahvazi; Mohammad ali zare chahouki
Volume 23, Issue 2 , September 2016, , Pages 287-275
Abstract
Artificial Neural Network (ANN) is new information processing structures that uses special methods for biological neural networks. The main purpose of this study was to modeling of Seidlitzia rosmarinus distribution in northeast rangelands of Semnan by ANN model. For this purpose, vegetation sampling ...
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Artificial Neural Network (ANN) is new information processing structures that uses special methods for biological neural networks. The main purpose of this study was to modeling of Seidlitzia rosmarinus distribution in northeast rangelands of Semnan by ANN model. For this purpose, vegetation sampling was carried out in each vegetation type along three transects of 750 m, on which 15 plots were established with an interval of 50 m. Soil samples were taken from two depths of 0-20 cm and 20-80 cm in starting and ending points of each transect. To provide the prediction map of plant species distribution, different layers of environmental factors used in the model are required. The geostatistics method was applied for mapping soil properties based on the prediction model obtained from ANN method for S. rosmarinus. The back-propagation neural network with three-layer- perceptron network was designed to generate the ANN model and seven neurons in the input layer, ten neurons in the hidden layer, and one neuron in the output layer were used. The accuracy of the prediction map was tested with actual vegetation maps and the Kappa coefficient was calculated to be 72%, indicating a very good agreement. Results showed that this species is distributed in rangelands with a pH of 8.1-8.3, an EC 0.22-0.26 dS/m, in a silty-sandy textured soil, and an altitude of 1600-1750 meters. it is highly correlated with lime and pH in two depths.
Mohsen Yousefi; leila kashi zenouzi
Volume 22, Issue 2 , August 2015, , Pages 240-250
Abstract
The aim of this study was to determine some factors affecting dust storms phenomenon using different methods. In order to determine the best-input combination, variable reduction techniques such as factor analysis (maximum likelihood, principal component analysis), Gama test, and multivariate forward ...
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The aim of this study was to determine some factors affecting dust storms phenomenon using different methods. In order to determine the best-input combination, variable reduction techniques such as factor analysis (maximum likelihood, principal component analysis), Gama test, and multivariate forward regression analysis were used. Each of these methods presented different combinations used by feedforward neural network model, with Levenberg–Marquardt algorithm and multivariate forward regression with R²=0.87 and RMSE=0.04 was selected as the best suitable combination of neural network model. In addition, monthly and seasonal data were applied by neural network using the best-input combination, and the simulation of dust storm phenomenon was done in summer and spring during the months of April, May, June, July, August and September with a higher correlation coefficient and lower mean square error, due to the good distribution of the dust storm data. The results showed that based on these methods used in this study, dominant wind speed, horizontal visibility, continuity and average of wind speed were the most important factors affecting dust storm phenomenon in Yazd province.
Mohammad reza Jamalizadeh Tajabadi; Ali reza Moghadam nia; Jamshid piri; Mohammad reza Ekhtesasi
Volume 17, Issue 2 , September 2010, , Pages 205-220
Abstract
Dust storms are common climatic events in arid, semi arid and desert regions of the world. These events impact human resources by foundation losses, every year. Accurate prediction of these events can be effective for decision support in environmental, health, army, and other related fields. An artificial ...
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Dust storms are common climatic events in arid, semi arid and desert regions of the world. These events impact human resources by foundation losses, every year. Accurate prediction of these events can be effective for decision support in environmental, health, army, and other related fields. An artificial neural network is a method which can predict nonlinear problems. In this study we attempted to predict dust storms and low visibility in Zabol city using synoptic data. Result indicates that this method is somewhat successful and appears that via identification of much more dust storm occurrence process, we can do more accurate prediction.