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Showing 10 results for Neural Network

Arash Malekian, Mahro Dehbozorgi, Amir Hoshang Ehsani,
Volume 15, Issue 36 (6-2015)

Drought is one of the most destructive natural disasters in human societies that cause irreparable impacts on agriculture, environment, society and economics. So, awareness of occurrence of droughts can be effective in reducing losses. In this study, in order to modeling and forecasting drought severity in a 37 year time period (1971-2007) in 21 meteorological stations, located in the cold semi-arid region of north-west Iran, artificial neural networks was used. The input data was annual rainfall data and annual drought precipitation index for all stations that 80% of the data (1971-2000) used for training the network and other 20% (2001-2007) used for testing it and in the next step drought severity predicted for the years 2008 to 2012 by the trained algorithm without using actual and existed data in this period. The appropriate structure for the network, based on Multi Layer Perceptron with three hidden layer, Back Propagation algorithm, Sigmoid transfer function and 10 neurons in middle layer. The results show that the artificial neural networks are well able to predict the non-linear relationship between rainfall and drought as it can simulate drought precipitation index values largely consistent with the real values with more than 97% regression and less than 5% error. So, drought can be predicted by this method in future and also it is useful in water resources management, drought management and climate change. 
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Volume 16, Issue 42 (12-2016)

In this study is predicted the groundwater level of Sharif Abad catchment using some artificial intelligence models. For this purpose used of monthly groundwater levels for modeling in the three observed wells located in the Sharif Abad watershed of Qom. To compare the results of the hybrid model of wavelet analysis-neural network (WNN), genetic programming (GP) multiple linear regression (MLR) and artificial neural network (ANN), two criteria of root mean squared error (RMSE) and nash-sutcliffe coefficient of efficiency (E) is used. The results of the study indicated that the WNN models provide more accurate monthly groundwater level predicted in compared to the ANN, GP and MLR models so the nash-sutcliffe coefficient in WANN model for piezometers 1, 2 and 3 are 0.98, 0.98 and 0.95, respectively.


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Volume 17, Issue 47 (12-2017)

Suspended particles management is one of the important issues in controlling the air pollution of cities. These particles cause and develop heart and respiratory diseases in people. Mashhad is considered as one of the main and populous cities of Iran. Because of its climatic conditions and its tourism, the city is at the highest risk of this type of pollution. We attempted to use the multi-layer perceptron (MLP) artificial neural network and a Markov chain model to predict PM10 concentrations the city. We applied hourly data of CO, SO2, PM2.5 and temperature in late March and April 2015. Out of 1488 data series, 1300 data were used for network training and 188 data were used for validation. The results indicated the optimal performance of these methods in predicting of the amount of pollutants and also the probability of occurrence of hours with different quality of contamination. The best MLP artificial neural network model predicted the amount of pollutant particles with a coefficient of determination (R2) 0.88, index of agreement of  0.91 and a mean square error of  2.26. Also, the Markov model with average absolute error predicted about 0.1 percent of the probability of transferring the condition and the continuation of different states of air pollution caused by suspended particles.
Dr Javad Sadidi, Dr Hani Rezayan, Mr Mohammad Reza Barshan,
Volume 17, Issue 47 (12-2017)

Due to the complexity of air pollution action, artificial intelligence models specifically, neural networks are utilized to simulate air pollution. So far, numerous artificial neural network models have been used to estimate the concentration of atmospheric PMs. These models have had different accuracies that scholars are constantly exceed their efficiency using numerous parameters. The current research aims to compare Elman and Jordan recurrent networks for error distribution and validation to estimate atmospheric particular matters concentration in Ahvaz city. The used parameters are relative humidity, air pressure, and temperature and aerosol optical depth. The latter one is extracted from MODIS sensor images and air pollution monitoring stations. The results show that Jordan model with RMSE of 219.9 milligram per cubic meter has more accuracy rather than Elman model with RMSE of 228.5. The value of R2 index that shows the linear relation between the estimated from the model and observed values for Jordan is equal to 0.5 that implies 50% estimation accuracy. The value is because of MODIS spatial resolution, inadequacy in numbers as well as spatial distribution of meteorological station inside the study area. According to the results of the current research, it seems that air pollution monitoring stations have to increase in terms of numbers and suitable spatial distribution. Also, other ancillary data like volunteer geographic air pollution data entry using mobile connected cheap sensors as portable stations may be used to implement more accurate simulation for air pollution.

Fatemeh Mohammadyary, Hamidreza Pourkhabbaz, Hossin Aghdar, Morteza Tavakoly,
Volume 18, Issue 50 (6-2018)

Land-use change is one of the most important challenges of land-use planning that lies with planners, decision-makers and policymakers and has a direct impact on many issues, such as economic growth and the quality of the environment. The present study examines the land use change trends in Behbahan city for 2014 and 2028 using LCM in the GIS environment. Analysis and visibility of user variations, carried out in two periods of Landsat satellite images of 2000 (ETM + sensor) and 2014 (OLI sensors), and land cover maps for each year. The transmission potential modeling was performed by using the multi-layer perceptron artificial neural network algorithm using six independent variables and the distribution of changes in user usage were calculated by Markov chain method. The results of the prediction showed that the most reduction in the changes is the degradation of the rangelands and the greatest increase in the area of agricultural use. According to the horizontal tabulation results of the 2028 map, it can be stated that from the total area of the area 28336.22 hectares of land were unchanged and 33223.78 hectares of land use change. Also Rangeland and forest degradation during this time period can be a danger to urban planners and natural resources.
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Alireza Entezari, Fatemeh Mayvaneh, Khosro Rezaie, Fatemeh Rahimi,
Volume 18, Issue 51 (7-2018)

Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness between the years 1382-1392 were used. In the First step، Tmrt parameter was calculated in the Ray Man software environment. Then UTCI and PMV index values were calculated using Bioklima software. The results showed that the most severe cold temperature stress on PMV index is in the winter and late autumn and UTCI index in January and February are the coldest stress. The power of neural networks, prediction of future performance network (generalized orientation) it simply is not possible and the new model presented in this paper have been restricted Boltzmann machine-based neural networks or neural networks is used deep belief. Using this structure, metrics Mean Squared Error (MSE) and mean absolute percentage error (MAPE) benchmark ate for seven indexes derived from data gathered by three factors related to the occurrence of weather conditions and other indicators of thermal comfort of human the system was evaluated. Assessment by dividing the data into training and testing parts, and the ratios have been of two-thirds, fifty percent and one-third And two benchmark MSE and MAPE were calculated. The proposed system performance in forecasting the human thermal comfort is desirable.

Dr Iran Salehvand, Dr Amir Gandomkar, Dr Ebrahim Fatahi,
Volume 20, Issue 59 (1-2021)

Rainfall prediction plays an important role in flood management and flood alert. With rainfall information, it is possible to predict the occurrence of floods in a given area and take the necessary measures. Due to the fact that the three months of January, February and March are most floods and most precipitation is occurring this quarter, this study aimed to investigate the factors affecting precipitation and modeling of this quarter. For precipitation modeling, the monthly rainfall data of the Hamadid and Baranzadeh station in the statistical period (1984-2014) for 30 years as a dependent variable and climatic indexes, large-scale climatic signals including sea surface temperatures and 1000 millimeter temperatures Altitude of 500 milligrams, 200 milligrams of omega and climatic elements have been used as independent variables. Due to the nonlinear behavior of rainfall, artificial neural networks were used for modeling. Factor analysis was used to determine the best architecture for entering the neural network. For prediction of precipitation, the data that showed the most relationship with precipitation was used in four patterns, in January the fourth pattern with entropy error was 045/0, the number of input layers was 91, the best makeup was 15-1, and the correlation coefficient was 94% Was. In February, the third pattern with a correlation coefficient of 97%, entropy error, was 0.36. Percentage, number of input units was 8 units, and the best type of latency layout was 10-1. The precipitation of March with all patterns was high predictive coefficient. The first pattern with entropy error was 0.038, the number of input units was 67, the hidden layer arrangement was 17-1, the correlation coefficient was 98%.
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Mr Mozaffar Mohamadkhani, Dr Zeynab Karke Abadi, Abbas Arghan,
Volume 20, Issue 59 (1-2021)

The structure of urban resilience consists of four dimensions: social, economic, institutional and physical. In the desired situation, a resilient city has a strong local society with a dynamic and sustainable economy that is governed institutionally and institutionally in a participatory manner. As a result of these super-physical factors, the city is formed in a cohesive manner and no withering is observed at any point. The purpose of this study is to assess the resilience and stability of Semnan in the face of natural hazards (earthquake). The research method in this study is descriptive-analytical and its statistics and information have been obtained using a questionnaire. The statistical population of this study was citizens living in Semnan city. Using Cochranchr('39')s formula, a sample of 384 people was selected from them by random sampling method. To assess the validity (validity), using face validity, the opinions of related people were examined and its reliability was assessed using Cronbachchr('39')s test in the SPSS software environment equal to / 863. It was found to indicate high coordination and reliability of the data. In data analysis, descriptive and inferential statistical tests were used in Spss software. percentage; At the level of inferential statistics, Pearson correlation coefficient and sample titech test as well as neural network model were used to examine the relationship between variables. Findings showed that the socio-cultural dimension with an average rank of 2.59 and the physical dimension with an average rank of 3.05 and the economic dimension with an average rank of 2.17 and finally the institutional-organizational dimension with an average rank of 2.56 show the current situation of resilience in Semnan.
Dear Dariush Abolfathi, Dr Aghil Madadi, Dr Sayyad Asghari,
Volume 22, Issue 66 (10-2022)

The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLP's neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.

Ali Hashemi, Hojjatollah Yazdanpanah, Mehdi Momeni,
Volume 24, Issue 75 (2-2025)

Studying the effect of climatic variables on vegetation indices (Case study: Orange orchards in Hassan Abad, Darab County)
Climatic variables are the most significant factors affecting vegetation changes. Nowadays, the satellite imagery is widely used to investigate the effect of fluctuations in climatic variables on vegetation changes. This research aims to investigate the effect of climatic variables of precipitation, temperature, and humidity on changes in vegetation indices of orange orchards in Hassan Abad, Darab County using satellite data. Hence, observational data, including orange tree phenology data and meteorological data on the agricultural weather station have been collected for over 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on the territorial data and 1:25000 maps of the Iran National Cartographic Center. These images were used to calculate the remote sensing vegetation indices including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on the NDVI and EVI dependent variables. To determine the significance of each of the independent variables in predicting the dependent variables, the artificial neural network method was used. Findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity and maximum humidity with values (0.39, 0.3, 0.13, 0.1 and 0.06) had the greatest effect on EVI, respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients (0.2, 0.28, 0.22, 0.11 and 0.17), respectively. Finally, ARMAX regression method was used to increase the explanatory power of the model. Results showed that this method could increase the explanatory power of the model and reduce the forecasting error.

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