Elham Khodabandehloo; Mohsen Azadbakht; Soheil Radiom; Davood Ashourloo; Abas Alimohammadi
Abstract
Rapid increase of the world population growth and the demand for food security makes increasing yield as an essential strategy for solving the food supply problem. What is more, because of the restrictions in increasing crop cultivation areas and the decrease in some crops such as wheat in Iran, increasing ...
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Rapid increase of the world population growth and the demand for food security makes increasing yield as an essential strategy for solving the food supply problem. What is more, because of the restrictions in increasing crop cultivation areas and the decrease in some crops such as wheat in Iran, increasing the yield potential can be an effective way to respond to this requirement. Fusarium Head Blight (FHB) is one of the most important wheat diseases and for prediction FHB some methods have already been developed in the USA, Canada, Argentina and Brazil. As there is no model for predicting FHB in Iran, in this study, a method for predicting severity of FHB based on spatial analysis and using environmental parameters and meteorological data was developed for the Moghan, which is in the northwest of Iran. An Internet of Things (IoT) network was established in the study area for measurement of environmental data, including relative humidity, rainfall and air temperature for evaluating the developed model. Random Forests (RF) and extracted indices were used for predicting FHB severity and calculating the relative importance of the indices. We evaluated FHB for the period of 1389 to 1396 and the results show the effectiveness of the developed model and the capability of IoT and spatial analysis for predicting FHB.
vahid ahmadi; Abbas Alimohamadi
Abstract
Drought evaluation is important in terms of spatial and temporal for planning to reduce damages in the Kordestan province. In this research, Standardized Precipitation Index and the Enhanced Vegetation Index have been used from the extracted satellite images for determinants of drought. so, the statistical ...
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Drought evaluation is important in terms of spatial and temporal for planning to reduce damages in the Kordestan province. In this research, Standardized Precipitation Index and the Enhanced Vegetation Index have been used from the extracted satellite images for determinants of drought. so, the statistical data of Meteorological stations including maximum monthly temperatures, total annual precipitation and the images of MODIS sensor have been employed. By comparing meteorological parameters such as average annual temperature, rainfall and the comparison of maps of the Standard Precipitation Index and Enhance Vegetation Index, the condition of drought has been investigated in the region in a 17-year period. The results of the two SPI and EVI indices indicate that the drought is due to rain changes have in the west-to-east direction. This phenomenon is more severe in the eastern regions whereas vegetation sensitivity and the fluctuation of its variations have been affected by precipitation changes in the north-to-south direction over the region. In this way, the southern regions have shown higher sensitivity. Southern regions are generally more vulnerable to droughts, especially in the south-east of the province. Regions with high drought sensitivity make up about 10 percent of the area regarding the regions in the province, whereas 91 percent of the area of regions with very high drought sensitivity is places where the landuse involves growing wheat with rain water.
َAmirhossein Vahdat; Abas Alimohammadi
Abstract
The models of the association between land use and air pollution have wide applications in urban studies, but the land-use role and its different parameters effective on the variability of air pollution concentration in various hours can be used for more accurate Spatio-temporal prediction of pollution. ...
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The models of the association between land use and air pollution have wide applications in urban studies, but the land-use role and its different parameters effective on the variability of air pollution concentration in various hours can be used for more accurate Spatio-temporal prediction of pollution. In this study, to make Spatio-temporal prediction of CO pollutants using hourly land-use regression (LUR), the effective parameters on Spatio-temporal variation of this pollutant are investigated during the day and night. The hourly data are collected from 21 air pollution monitoring stations for the summer in Tehran and the predictive parameters including density and distance from different variables such as road network, vegetation, elevation, and different land-use are generated in the geographic information system (GIS). A general model and 8 hourly models are created at 3 am, 6 am, 9 am, 12 noon, 3 pm, 6 pm and 12 midnight. The coefficient of determination (R2) of the created model is equal to 0.7898, and it shows that the model has an outstanding performance. By analyzing the generated hourly models, because of the differences in the parameters used in these models, it is denoted that both temporal variability and spatial variability play effective roles in forming the models during the day and night. The coefficient of determination (R2) of the hourly models ranges from 0.51 to 0.92 in which the lowest one and the highest one are related to the noon hours’ models and the nocturnal hours’ models, respectively. The parameters including local access roads and official/commercial areas have the most effect on increasing CO pollutants during the day and night, and the parameters including green space, sports, and medical centers lead to the locations with lower CO pollutants concentration.
Ayoub Moradi; Hadiseh Babaei; Abbas Alimohammadi; Soheil Radiom
Abstract
The increasing shortage of the renewable water resources in the country has made the farm water needs estimation to become as one of the important priorities in agricultural water management. Farm water needs are normally controlled by climatologic factors. It equals to the reference evapotranspiration ...
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The increasing shortage of the renewable water resources in the country has made the farm water needs estimation to become as one of the important priorities in agricultural water management. Farm water needs are normally controlled by climatologic factors. It equals to the reference evapotranspiration which is corrected by a scaling factor associated to the crop kind and to local characteristics. In this research, using Landsat satellite imagery, we estimated and compared the crop coefficients for main agricultural crops in the Moghan cultivation industry, from two procedures: the first based on evapotranspiration measuring, and the second based on NDVI measuring. The comparisons in the case of the five main crops showed that the Root Mean Square Errors are within an acceptable range, leass than 0.28. In the following, the evapotranspiration based crop coefficient has been used in order to estimate farm water needs. Farm's water needs are indeed estimated by six methods: a combination of two actual evapotranspiration and three reference evapotranspiration ways. Among the six methods, the Metric/PenmanMonteith method was selected for final step, i.e. farm irrigation needs. The farm irrigation needs is equivalent to farm water need minus effective rain. We compared four different ways for estimating the effective rains but preferred the FAO method assigned for low slopes. Based on our results, farm irrigation needs in the Moghan cultivation industry range from 270 mm (for rainfed barley) to 1500 mm (olive groves). Statistical investigation in three years data revealed a dependency between yield performance and evapotranspiration rate. In addition, it showed that yield performance is correlated with crop spectral indices such as NDVI, LAI and SAVI. The primary goal of this research is to estimate local agricultural crop coefficient in the Moghan cultivation industry. The second goal is to investigate of relationships between crop coefficient and crop spectral indices in order to make the crop coefficient estimable directly from spectral indices.
Hamed nematollahi; Davoud Ashourloo; Abas Alimohammadi; Elham Khodabandehloo; Soheil Radiom
Volume 10, Issue 3 , January 2019, , Pages 105-122
Abstract
One of important objectives in sustainable agriculture is preservation of healthy ecosystems with focus on natural aquatic and terrestrial resources management in order to accomplish food security at local and global scales. Time-series remotely sensed datasets are precious and valuable resource of temporal ...
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One of important objectives in sustainable agriculture is preservation of healthy ecosystems with focus on natural aquatic and terrestrial resources management in order to accomplish food security at local and global scales. Time-series remotely sensed datasets are precious and valuable resource of temporal and spectral information that could support researchers to access field management goals. Farm management have been always encountered some challenges such as lack of access to quantitative and qualitative information of agricultural crops. This research aims to develop crop and field condition indices using time-series of NDVI (Sentinel-2) and crop type maps of Moghan Agro-Industry (MAI) in 2016-2017 and also Shahid Rajaei Agro-Industry (SRAI) in 2017-2018. Then we tried to identify parts of the fields that are affected by Environmental factors such as disease, pest, weed, soil-related deficiencies and uneven distribution of water due to Inefficient irrigation system. To this end, Time-series of NDVI for four crops (wheat, maize, alfalfa and sugar beet) in various fields was provided. Finaly, field and crop condition indices were developed to show the variations of crop in each field and also the fields in comparison with each other. Finally, the proposed indices showed high accuracy with ground observations. The results were 88.88% for Alfalfa fields in MAI, and 94.11% for wheat fields in SRAI. After evaluation of the results of indices with ground observations, it was revealed that where field (homogeneity) index is low, growth limiting factors are activated.
B Tashayo; A Alimohammadi
Volume 9, Issue 3 , February 2018, , Pages 91-110
Abstract
This article develops and demonstrates a new quantitative modeling approach for environmental health impact assessment of traffic scenarios. For this purpose, two models based on hierarchical fuzzy inference system (HFIS) are developed. In order to develop HFIS for modeling the effect of transportation ...
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This article develops and demonstrates a new quantitative modeling approach for environmental health impact assessment of traffic scenarios. For this purpose, two models based on hierarchical fuzzy inference system (HFIS) are developed. In order to develop HFIS for modeling the effect of transportation system on the PM2.5 concentrations, the data from an air dispersion model are utilized. There are several advantages to this approach such as modeling the spatial variation of PM2.5 with high resolution, suitable processing requirements, and consideration of interaction between emissions and meteorological processes. Moreover, the resulting fuzzy landuse regression (LUR) is capable of using accessible qualitative and uncertain data. In order to develop HFIS for modeling the impact of traffic-related PM2.5 on health, a metric derived from epidemiological studies is employed. The suggested model improved the metric capabilities by modeling the uncertainty of relationships among parameters and parameter value. Two solutions are used to improve the performance of both models. First, the topologies of HFISs are selected according to the problem. Second, used variables, membership functions and rule set is determined together through learning. We examine the capabilities of the proposed approach with assessing the impacts of three traffic scenarios to deal with air pollution in Isfahan, Iran and compare the accuracy of the results with representative models from existing literature. The models are first developed based on the current traffic conditions. Then; Low Emission-Zone and Odd/Even scenarios are examined. The examination shows that, they are the most and least effective scenarios in reducing air pollution and improving environmental health, respectively. The obtained results demonstrate that the proposed approach has desirable accuracy; beside that the model can provide better understanding of phenomena and investigating the impact of each of parameters for the planners.
V Ahmadi; A Alimohammadi; J Karami
Volume 9, Issue 2 , December 2017, , Pages 61-78
Abstract
Management and planning of urban water supply in metropolis is very important. Development of the region urban and make cities to metropolis and increase of effective complex factor on water usage in the cities make consumption management and supply and distribution Water difficult. So rule extraction ...
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Management and planning of urban water supply in metropolis is very important. Development of the region urban and make cities to metropolis and increase of effective complex factor on water usage in the cities make consumption management and supply and distribution Water difficult. So rule extraction plays an important role in exploring patterns over data and decreasing complex. Rough Set Algorithm, which was developed in 1980s by Pawlak, is a powerful and flexible method to deal with uncertain and ambiguous data which has been used in this research to extract dominant rules over data set. The method used in this paper is combination of the rough set and genetic algorithms from data mining methods to develop rule extraction and data classification of water usage in Tehran city as the studying area. Socio-economic, environmental, time and water consumption and management zones have been used as the explanatory variables for prediction of the water use that database divided to 2 part 60% for result extraction and 40% as test set. Independent test sets have been used for evaluation of the accuracy of the extracted rules. Results have shown that, combination of the genetic algorithms and Rough Set leads to extraction of more reliable rules. Classification accuracy of the extracted rules from Rough sets was 77 percent. But optimization of rules by combination of the genetic algorithm with Rough sets, resulted in classification accuracy of 88 percent in 6th generation with average speed of convergence. By using the same speed of convergence in the accuracy increased to 92 percent in 10th generation. According to the extracted rules, important effective factors on annual water consumption are respectively the resident population, water price, population density, family size, spatial location (latitude), education levels, and per capita green spaces.
, A.A Matkan; , A. Alimohammadi; , B Mirbagheri; , K Akbari; , M Tanasan
Volume 9, Issue 1 , October 2017, , Pages 17-36
Abstract
Commensurate with the complexity of human behavior, social systems are complicated. Population management in these systems are crucial and need to spend too much cost. Because of the interaction between humans and the environment and then the impact of these interactions on social systems in the process ...
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Commensurate with the complexity of human behavior, social systems are complicated. Population management in these systems are crucial and need to spend too much cost. Because of the interaction between humans and the environment and then the impact of these interactions on social systems in the process of population movements, there is a need to identify and study these interactions, especially in emergency situations.In this study, the results of agent based geosimulation of pedestrian movements and fire simulation at Hafte-Tir subway station were used to investigate the behavior of individuals and the environment during fire. Then, the discomfort indices, including environmental and human-environmental indicators, were calculated to examine the effect of the environment and agents on the movement process. This research has introduced two new discomfort indices i.e. environmental index AM1 and environmental-humanity index AM2 to evaluate the behavior of individuals and the environment during the fire. The innovation of these indices relates to the integration of the results of the agent based simulation and the fire simulation in the environment and after that using of visibility, in addition to the interactions of individuals with each other and their interactions with the physical components of the environment. Calculating results of indices and the results of people movement’s simulation in the station represented an inverse relationship between the level of discomfort and speed of crowd in the station. Also, the discomfort induces in the successful environmental scenario shows a reduction in the discomfort in hot spots rather than current situation scenario. The use of agent based geosimulations and the result of discomfort indices in different periods of crisis, can contribute population management strategies and emergency evacuation.
E Khodabandehloo; A Alimohamdadi; A Sadeghi-Niaraki; A Darvishi Boloorani; A.A Alesheikh
Volume 8, Issue 1 , November 2016, , Pages 1-18
Abstract
Dust storm has been one of the most important challenges of western Asia. This phenomenon has been intensified due to the drought and has many negative effects on people's lives. Since this region located in a dust belt in the world, it is necessary to explore different aspects of this phenomenon ...
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Dust storm has been one of the most important challenges of western Asia. This phenomenon has been intensified due to the drought and has many negative effects on people's lives. Since this region located in a dust belt in the world, it is necessary to explore different aspects of this phenomenon is well. Predictive and modeling of this phenomenon can be prevented of jeopardizing the lives of millions of people. So present a Regional Model to assess different aspects of this phenomenon is necessary. Since climate and weather elements are constantly changing, the spatiotemporal model should be used for modeling and visualization. Hence, a model for estimating dust emission has been designed and developed and Geographic Information System (GIS) spatial modeling capabilities and remote sensing (RS) data (wind speed, soil moisture, soil texture and digital elevation model) are used. The model which is called DustEM calculates horizontal dust emission. In this study, modeling is done for 2001 to 2007 and model’s output is evaluated by MODIS AOD and for dictating hot spot area output is clustered in 3 categories contain high, medium and low with threshold 0.3 and 0.6 for AOD. Accuracy index mean for the study period was 73.6% and show high precision of model in detecting hot spot area.
R Hosseini; A Alimohammadi; M.H Ghasemian
Volume 8, Issue 2 , November 2016, , Pages 17-34
Abstract
Change detection methods are powerful tools to present the changes on the Earth’ surface. The multi-scale approaches which proceed the observations at coarser and finer scales, can be applied to maximize the accuracy of the change maps. The multi-scale approach, based on discrete wavelet, has been ...
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Change detection methods are powerful tools to present the changes on the Earth’ surface. The multi-scale approaches which proceed the observations at coarser and finer scales, can be applied to maximize the accuracy of the change maps. The multi-scale approach, based on discrete wavelet, has been applied in this research. In addition to the spectral information, the contextual or local information- available in the image are set in the processing. The wavelet technique is exploited in many processing fields of images. The ability of the wavelet technique has been applied for the change-detection, based on the satellite images in this study. The necessary parameters for the wavelet modification are the quantity decomposition levels and kind of mother wavelet. Thus The effect of the mother wavelet boir3/7 and db4 and the levels of decomposition s=1 to s=6 on the final change detection map have been assessed . All the results have been stated on the basis of the detection accuracy kappa coefficient and overall accuracy. The results reveal the influences of the mother wavelet and levels of decomposition on the final change detection map. The Change detection map, using t bior3/7mother wavelet, reveals higher overall accuracy and better kappa coefficient in proportion to bd4 mother wavelet. It is 0/7966 and 89/8013 for band 3 of Mother wavelet bior3/7 and 9/8013 & 0/7966 for mother wavelet db4. The next parameter being investigated here is related to the analysis surfaces influence on the precision of the change detection map. It increases to the level 3 of analysis and then decrease down. Eventually, most of the overall precision and kappa coefficient is related to the analysis level 3 of both mother wavelet. A comparison has been also conducted between the wavelet technique and the three methods image differencing , image ratio and supervised classification. The final review reveals the priority of the wavelet technique, as it presents better results.
