Showing 6 results for Artificial Neural Networks
B. Najafi, M. Zibaei, M. H. Sheikhi, M. H. Tarazkar,
Volume 11, Issue 1 (4-2007)
Abstract
In this study wholesale prices of selected crops, namely, tomato, onion and potatoes in Fars province were predicted for various time horizons by using common methods of forecasting and artificial neural networks (ANN). Monthly data from September 1998 to June 2005 period were obtained from Ministry of Jihad-e Agriculture. For comparing different methods data selected from September 1998 to December 2004 were utilized, and latest six - month data were mainly used to monitor the power of prediction. The MAE, MSE and MAPE criteria were used for comparing the ability of different forecasting methods. Results of this study showed that ANN had the lowest error in prediction of prices for one - to three - month periods, but for six - month prediction, all forecasting methods were not statistically different.
Afkhami, Dastorani, Malekinejad , Mobin,
Volume 14, Issue 51 (4-2010)
Abstract
Drought is a natural feature of the climate condition, and its recurrence is inevitable. The main purpose of this research is to evaluate the effects of climatic factors on prediction of drought in different areas of Yazd based on artificial neural networks technique. In most of the meteorological stations located in Yazd area, precipitation is the only measured factor while generally in synoptic meteorological stations in addition to precipitation some other variables including maximum and mean temperature, relative humidity, wind speed, dominant wind direction and the amount of evaporation are also available. In this research it was tried to evaluate the role of the type and number of meteorological factor (as inputs of ANN model) on accuracy of ANN based drought prediction. Research area is a part of Yazd province containing only one synoptic and 13 non-synoptic meteorological stations. Three-year moving average of monthly precipitation was the main input of the models in all stations. The type of ANN used in this study was time lag recurrent network (TLRN), a dynamic architecture which was selected by evaluation of different types of ANN in this research. What was predicted is the three-year moving average of monthly precipitation of the next year, which is the main factor to evaluate drought condition one year before it occurs. For the Yazd synoptic meteorological station, several combinations of input variables was evaluated and tested to find the most relevant type of input variables for prediction of drought. However, for other 13 stations precipitation data was the only variable to use in ANN models for this purpose. Results in all stations were satisfactory, even where only one input (precipitation) was used to the models, although the level prediction accuracy was different from station to station. Result taken from this research, indicates high flexibility of ANN to cope with poor data condition where it is difficult to get acceptable results by most of the methods.
M. Gholamzadeh, S. Morid, M. Delavar,
Volume 15, Issue 56 (7-2011)
Abstract
Application of drought early warning system is an important strategy for drought management. It is more pronounced in the arid regions where dams have vital role to overcome water shortages. This papers aims to develop and apply such a system that includes three main components, which are 1) drought monitoring, 2) forecasting inflows and water demands and 3) calculation of a warning index for decision about drought management. The system is presented for the Zayanderud Dam. For this, the future six months river inflows and demands are forecasted at different probabilistic levels using the artificial neural networks and considering respected uncertainties. Also, five drought levels are indicated based on the historical records of dam’s storage and the self organizing feature map technique. Furthermore, a drought alert index (DAI) is defined using current storage of dam and the forecasted flows and demands. Finally, the different alert levels are estimated, which vary from normal to sever water scarcity. The results showed that application of the designed warning system can have effective role in the dam’s operation, rationing policy and reducing drought losses.
K. Bayat, S. M. Mirlatifi,
Volume 16, Issue 61 (10-2012)
Abstract
Global solar radiation (Rs( on a horizontal surface in the estimation of evapotranspiration of plants and hydrology studies is an important factor. Average daily global solar radiation on a horizontal surface was estimated by artificial neural networks (ANNs) and five empirical models including FAO (No.56), Hargreaves-Samani, Mahmood-Hubard, Bahel and Annandale. The weather data was selected from Karaj, Shiraz, and Ramsar weather stations, which have arid, semi arid and very humid climates (based on De Martonne classification). Daily solar radiation was measured at the three sites selected. The ANN, with actual duration of sunshine and maximum possible duration of sunshine as input parameters, generated daily solar radiation estimates with highest level of accuracy among all models tested. Rs estimates by ANNs with only temperature indices as input and by Hargreaves-Samani, Annandale and Mahmood-Hubard, which are all temperature oriented models, had lower accuracy at all three sites. In contrast, ANNs with actual duration of sunshine and maximum possible sunshine hours as inputs in Karaj, Shiraz and Ramsar station with root mean square error (RMSE) of 2.08, 1.85 and 2.05 Mj m-2 day-1 respectively were the best models. After ANNs, FAO-56 model which is based on sunshine hours produced results closer to the measured values. Rs estimates by ANNs with only temperature indices as input and by Hargreaves-Samani, Annandale and Mahmood-Hubard which are all temperature oriented models, had lower accuracy at all the three sites. These models are not appropriate for estimating daily global solar radiation.
M. Hayatzadeh, M. R. Ekhtesasi, H. Malekinezhad, A. Fathzadeh, H. R. Azimzadeh,
Volume 21, Issue 1 (6-2017)
Abstract
Soil erosion is undoubtedly one of the most important problems in natural areas of Iran and has destructive effects on different ecosystems. Considering that calculation of the sediment rate in sediment stations and direct measurements of erosion process is costly and difficult, it is critical to find ways to accurately estimate the amount of sediment yield in catchments especially in arid and hyper arid areas because of their high ecological sensitivity. One of the most commonly used methods in these areas is the sediment rating regression method. Therefore, in this study sediment observed data for 48 events (the corresponding discharge and sediment) in a 23-year period from Fkhrabad basin (Mehriz) were compared to the estimated data obtained from Multi-line rating method, extent middle class, middle class rating curve with correction factor QMLE, SMEARING correction coefficient FAO and Artificial Neural networks (ANNs). Finally, the accuracy of these methods were assessed using different evaluation criteria such as Root Mean Square Error (RMSE), coefficient of determination (R2) and the standard Nash (ME). Results showed that ANN outperformed the other methods with the RMSE, R2 and ME of 203.3, 0.86 and 0.66, respectively. The results suggest that these methods should be used cautiously in estimating the suspended sediment load in arid and hyper arid regions due to the nature of the observed data and temporal and seasonal flow systems in these regions. It was also indicated that the artificial neural network models have higher flexibility than other methods which makes them to be useful tools for modeling in poor data conditions.
F. Zarif, A. Asareh, M. Asadiloor, H. Fathian, D. Khodadadi Dehkordi,
Volume 26, Issue 2 (9-2022)
Abstract
An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.
