Elham Pourmaafi Esfahani; Ali Almodaresi; Mohammad Mousaei Sanjerehei; Hamed Hghparast
Abstract
Introduction: Today, dust phenomena are among the most important environmental hazards and pose a serious threat to human health and the environment. Dust in barley as one of the pollutants has various adverse effects and negative consequences, among which can be reduced growth and yield of agricultural ...
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Introduction: Today, dust phenomena are among the most important environmental hazards and pose a serious threat to human health and the environment. Dust in barley as one of the pollutants has various adverse effects and negative consequences, among which can be reduced growth and yield of agricultural products, intensification of damage caused by pests and plant diseases, increased road accidents due to reduced visibility, The cancellation of flights and the resulting financial losses, increased treatment costs, closure of industrial units, pollution of water resources, increased erosion of buildings, decreased efficiency of solar photovoltaic systems due to turbidity.Objective: Therefore, due to the importance of dust and in order to predict how dust is spread, the artificial neural network model was used. This model can be useful and cost-effective information for future implementation of air pollution control strategies and cost reduction.Material and methods: To model the dust distribution using artificial neural network model, statistics and meteorological information of Kashan synoptic station, which were recorded daily by the Environment Department in 1996, were used. The proposed neural network model has four input layers that include humidity, temperature, wind speed, wind direction and an output layer, the daily concentration of suspended particles is 2.5 micrometers per cubic meter. The model training process was performed using multilayer perceptron neural network and post-diffusion rule and using sigmoid membership function in Matleb software environment. In the neural network model, the number of neurons in the hidden layer and the appropriate number of rounds or IPAC to achieve the best neural network structure, with the least error for each model, were determined using trial and error. The number of neurons and apex for the model in 2017 is 15 and 37,000, respectively.Results and discussion: The correlation coefficient of the model for predicting PM2.5 concentration is equal to 0.80 which is obtained by comparing real data with simulated data. The validation results of the model with real data are close to 80%, so the neural network model can be used to predict PM2.5 concentration. According to the average regression diagram, the predicted values obtained from the model are closer to the diagonal axis and have no dispersion. Also, based on the results of the step-by-step regression method, it was determined that among the four variables used for relative humidity modeling, it has the most impact and importance in dust emission modeling.Conclusion: According to the accuracy and the results, this method can be used to predict the air pollution of Kashan caused by suspended particles. Due to the high capability of the perceptron neural network in predicting the concentration and distribution of dust, the application of this model can be a suitable and fast solution for predicting the amount and spread of dust.
Somayeh Moharami; Mahdi Sadeghi Pour Marvi; Rahman Sharifi
Abstract
Introduction: During the past two decades, computer aid models for simulation of heavy metals have been remarkably developed. Prediction of soil pollution plays an important role in pollution control and land management. But in large areas, collecting data in a direct way is challenging in terms of cost ...
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Introduction: During the past two decades, computer aid models for simulation of heavy metals have been remarkably developed. Prediction of soil pollution plays an important role in pollution control and land management. But in large areas, collecting data in a direct way is challenging in terms of cost and time. In recent years, the use of indirect methods such as artificial neural network (ANN) and other similar models to estimate heavy metals has been considered. There are 27 salt mines in Garmsar city. Of these, 16 mines are active. Salt extracted from these mines are used as one of the food spices. On the other hand, due to mining activities, the soils of this region may be contaminated with heavy metals. Therefore,in this study, the effectiveness of terrain and spectral indices for predicting total soil Cadmium (Cd) around the soils of Garmsar salt mines was evaluated by ANN – multilayer perceptron (MLP) model.Material and methods: For this research, 49 soil samples were collected from the 0-20 cm Physicochemical properties of soil samples such as percentage of clay, sand, silt, soil acidity (pH), electrical conductivity (EC) and lime percentage were determined. Total Cd concentration was measured by atomic absorption spectroscopy (AAS) (Varian, Spectra 220). All terrain attributes used in this study were derived from a digital elevation map (DEM) and to calculate the spectral indices, Landsat-8 OLI/TIRS bands image with a resolution of 30 meters were used. Twenty-five auxiliary data variables derived from a DEM and Landsat-8 were used to predict total soil Cd in the study area. Based on the auxiliary data obtained and the correlation coefficients between these data and the predicted total Cd value, 2 models were evaluated. The collected data were randomly divided into categories training and validation and were used to evaluate the MLP model.Results and discussion: The results of this study show that the auxiliary data extracted from landsat-8 bands (with the highest accuracy and lowest error rate) were the effective parameters in predicting soil contamination with Cd. Based on the results obtained from the evaluation of ANN performance in estimating total Cd, the value of root mean square error (RMSE) and coefficient of explanation (R2) were 0.05 and 0.95 for the first model and 0.10 and 0.80 for the second model. In model 1, saturation index (Sat I), grain size index (GSI), carbonate index (CrI), soil color index (color I) and gypsum index (GI) were important and main parameters in total Cd modeling. The results of the present study showed the high efficiency of the ANN model in predicting total soil Cd.Conclusion: Due to the development of machine learning models in the field of environmental engineering especially in simulation of heavy metals, having a turning point for their advancement is very important. The results of this research show that the MLP model is suitable for total soil Cd prediction and this method can save the cost of soil sampling and analysis. Therefore, it is recommended to validate the method applied in this study to prepare total soil Cd map in similar areas.
Omid Ashkriz; Babak Mirbagheri; Ali Akbar Matkan; Alireza Shakiba
Abstract
Introduction: Urban growth has accelerated in recent decades, therefore, predicting the future growth pattern of the city is very important to prevent environmental, economic, and social problems. The city of Tabriz has also experienced rapid growth of urban lands due to significant demographic changes, ...
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Introduction: Urban growth has accelerated in recent decades, therefore, predicting the future growth pattern of the city is very important to prevent environmental, economic, and social problems. The city of Tabriz has also experienced rapid growth of urban lands due to significant demographic changes, which requires accurate simulation of urban growth to prevent negative environmental and economic consequences. The purpose of this study was to evaluate the performance accuracy of the proposed machine learning algorithms by spatial cross-validation method in combination with the cellular automata model to simulate urban growth.Material and methods: In this study, to analyze urban land-use changes, Landsat satellite images related to the years 1997, 2006, and 2015 were classified using the support vector machine algorithm. In the next step, change potential maps of non-urban to urban areas were produced using random forest algorithms, support vector machine, and multilayer perceptron neural network for two periods of calibration (1997 and 2006) and validation (2006 and 2015) based on distance from the main roads, distance from the city center, distance from built-up areas, distance from the rivers and railways, as well as slope, elevation, and two-class (agricultural/barren) land use layer as effective factors in the growth of the city. Finally, using the cellular automata model, the growth simulation of Tabriz city based on land use and change potential maps obtained from machine learning algorithms for the mentioned periods was performed. To prevent over-fitting of algorithms to training samples and to obtain optimistic results, in the process of extracting optimal parameters of machine learning algorithms, the spatial cross-validation method was used to reduce the spatial correlation between training and test data.Results and discussion: The results showed that the random forest algorithm with the area under the ROC curve of 0.9228 compared to the support vector machine and multilayer perceptron neural network algorithms with 0.8951 and 0.8726, respectively, had a better performance in estimating the change potential of non-urban to urban areas. Furthermore, in comparison with others, the random forest also clearly showed local variations in potential change. Finally, the growth of Tabriz city was simulated using the cellular automata model based on the obtained change potential maps. Comparison of the prediction map in the validation period with the current situation of urban areas in 2015 showed that the accuracy of an urban growth simulation model based on random forest with a Figure of Merit index of 0.3569 compared to models based on support vector machine and artificial neural network was more accurate in allocating non-urban to urban lands with 0.3496 and 0.3434, respectively.Conclusion: As machine learning algorithms such as artificial neural networks, support vector machines, and random forest are capable of solving non-linear problems, using them is strongly recommended for urban growth simulation. Also, among the algorithms used in this research, the random forest algorithm based on ensemble learning has a higher advantage than the two-support vector machine and the artificial neural network algorithms.
Seyed Javad Hosseinifard; Hossein Shirani; ُSomaye Sadr; Hakimeh Hashemipour
Abstract
Introduction: Increasing concentrations of heavy metals in the environment have raised serious environmental concerns. Cadmium is one of the most toxic heavy elements in organisms and it has no biological role. So far, little research has been done on the status of heavy metals in pistachio orchards ...
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Introduction: Increasing concentrations of heavy metals in the environment have raised serious environmental concerns. Cadmium is one of the most toxic heavy elements in organisms and it has no biological role. So far, little research has been done on the status of heavy metals in pistachio orchards and factors affecting them. Therefore, the purpose of this study was to determine the relationship between cadmium extracted with DTPA in soil and other soil physical and chemical properties in agricultural soils of Rafsanjan using stepwise regression and artificial neural network modeling. Material and methods: In this study, 140 soil samples from two depths of 0 to 40 and 40 to 80 cm were collected from pistachio orchards in six regions of Rafsanjan suburb. Soil characteristics including available Cd and Zn concentration measured using DTPA, P concentration by Olsen method, percent of sand, clay and silt by hydrometer method, and pH and electrical conductivity of soil saturated extract by pH meter and EC meter, respectively, were measured. In order to investigate the relationship between available Cd and physical and chemical properties of the soil, stepwise regression and artificial neural network (multi-layer feed forward) were used. Results and dissussion: The results showed a significant and positive correlation between phosphorus and clay content and soil cadmium, a negative and significant correlation between Cd-DTPA and pH and clay percentage, and a positive correlation between available Cd and available Zn, total Zn, and total Cd. The results also showed that both modeling methods are accurate in estimating soil cadmium concentration, although the neural network model was more accurate. The R2 and root of mean square error for the neural network model were 84.3% and 0.01% for the test data, and 27.2% and 1.43% for the stepwise regression model, respectively. Also, cadmium concentration showed the highest sensitivity to zinc concentration and other parameters such as clay, pH, phosphorus, EC, and sand were in the next order of importance, respectively. These results confirm that due to the consumption of zinc containing fertilizers and the increased consumption of phosphate fertilizers which have high impurity in the amount of cadmium, an increase in soil cadmium concentration is observed in the pistachio orchards. Conclusion: Zinc and phosphorus fertilizers used in pistachio orchards have a significant impurity of cadmium that can cause soil contamination by cadmium due to its long-term use and absorption of this toxic element in pistachio plant and fruit. Therefore, while complying with national and international standards in the production and import of fertilizers, the use of these fertilizers should be optimized by analyzing and interpreting the results of soil and leaf analysis to reduce the risk of pistachio fruit contamination to cadmium.
Seyed Javad Sadatinejad,; Somayeh Angabini,; Mohammad Reza Mozdian fard
Volume 8, Issue 2 , January 2011
Abstract
Exact estimation of evapotranspiration is an important parameter in water cycle, study, design and management of irrigation systems. In this study, the efficiency of intelligent models such as fuzzy rule base, fuzzy regression and Artificial Neural Networks for estimating daily evapotranspiration has ...
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Exact estimation of evapotranspiration is an important parameter in water cycle, study, design and management of irrigation systems. In this study, the efficiency of intelligent models such as fuzzy rule base, fuzzy regression and Artificial Neural Networks for estimating daily evapotranspiration has been examined and the results are compared to real data measured by lysimeter on the basis of a grass reference crop. Using daily climatic data from Ekbatan station in Hamadan in western Iran, including maximum and minimum temperatures, maximum and minimum relative humidities, wind speed and sunny hours, evapotranspiration was estimated by the aforementioned intelligent models. The predicted evapotranspiration values from fuzzy rule base, fuzzy linear regression and artificial neural network provided root mean square error (RMSE) of 0.72, 0.86 and 0.74 mm/day and determination coefficient (R2) of 0.88, 0.86 and 0.84, respectively. The fuzzy rule base was hence found to be the most appropriate method employed for estimating evapotranspiration.
