H. Zare Abyaneh; M. Bayat Varkeshi
Abstract
Abstract
From Longley, the various equations for determining the runoff to water management are presented by the researchers that are widely used in hydrologic sciences. In this study by using observational data, was evaluated empirical, artificial neural network (ANN) and ca-active neuro-fuzzy inference ...
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Abstract
From Longley, the various equations for determining the runoff to water management are presented by the researchers that are widely used in hydrologic sciences. In this study by using observational data, was evaluated empirical, artificial neural network (ANN) and ca-active neuro-fuzzy inference system (CANFIS) models in estimation of runoff. For this purpose, by using climatic and physiographic information in three stations of Pole Zamankhan, Ghale Shahrokh and Sade Zayandeh Rood, runoff values were estimated from empirical models and intelligent models were compared to annual runoff values. Input parameters include rain, mean temperature, mininmum temperature and maximum temperature. The results showed that the artificial intelligent models had good accuracy in estimating runoff. Among the empirical methods, method of Di Souza was appropriate. Comparison statistical parameters between methods was showed that mean percent error (MPE) in ANN, CANFIS and empirical method was 7, 12 and 43 percent respectively that confirmed differences of between the methods is significant. Also, CANFIS model did not artificial improve ANN results. The results showed, with reduction of input variables from 4 parameters to one parameter of precipitation, modeling error reaches its maximum value (from MPE=7% to MPE=16%). Versus, the optimal structure of ANN had less sensitivity to remove the mean air temperature parameter (from MPE=7% to MPE=10%(. Therefore, according to empirical models required information limitations and high accuracy of artificial intelligent models, intelligent models application is recommended.
Keywords: Estimation of runoff, Empirical method, ANN, CANFIS, Zayandeh rood Basin
A.A. Sabziparvar; H. Zreabyaneh; M. Bayat
Abstract
Abstract
Soil temperature is one of the key parameters affecting most hydrologic and agricultural processes. Therefore, its measurement and prediction is very crucial. So far, the statistical regression methods have been used for estimation of soil temperature for specific location encountering with ...
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Abstract
Soil temperature is one of the key parameters affecting most hydrologic and agricultural processes. Therefore, its measurement and prediction is very crucial. So far, the statistical regression methods have been used for estimation of soil temperature for specific location encountering with lack or shortage of data. In this work, soil temperature data are estimated at six different depths for three typical climates (Zahedan, Tehran, Ramsar) by a new approach namely Adaptive Neuro-Fuzzy Inference System (ANFIS), and the results are compared with those of estimated by regression methods. In addition, the most important meteorological parameters (maximum temperature, minimum temperature, mean daily temperature, relative humidity, sunshine hour, and wind speed) which influence soil temperature at the study sites are used during the 15-years period (1992-2006) of study. The comparison of soil temperature data predicted by ANFIS and regression methods indicated that the performance of ANFIS model is 4% more accurate than regression methods. It was found that the accuracy of prediction using ANFIS model for arid climates of Zahedan and Tehran was 12% and 4.5% better than Ramsar (humid), respectively. The statistical comparison of the estimations derived by ANFIS model and the observed soil temperature data of drier climates showed that the coefficients of correlation (r) are reduced (up to 10%) for deeper layers. In contrast, for the humid climate of Ramsar, the model accuracy for near surface layers (5 and 10 cm) was up to 18% less than deeper layers (100 cm).
Keywords: Soil temperature, Regression models, ANFIS, Arid climate, Humid climate
H. Zreabyaneh; M. Bayat; S. Marofi; R. Amiri Chayjan
Abstract
Abstract
The present study is attempted to present the minimum required meteorological parameters for reference evapotranspiration estimation at Hamedan region of Iran from 1997 to 1998. Employing Pierson test, six meteorological parameters which are used by Penman-Montieth FAO-56 method including maximum ...
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Abstract
The present study is attempted to present the minimum required meteorological parameters for reference evapotranspiration estimation at Hamedan region of Iran from 1997 to 1998. Employing Pierson test, six meteorological parameters which are used by Penman-Montieth FAO-56 method including maximum and minimum air temperature, maximum and minimum relative humidity, wind speed and daily sunshine were composed and considered as 4 difference scenarios (called 1, 2, 3 and 4). These scenarios were applied to artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for reference evapotranspiration estimation of the area using the Matlab software. The results of the scenarios were evaluated using the actual reference evapotranspiration (lysimeter data). The results showed that increasing of number of input layers data could not be based as obtaining the more exact results. Using the scenario 2, which was based on minimum and maximum temperature as well as daily sunshine, showed more reliable results using the ANN and ANFIS methods. The root mean square error (RMSE), mean absolute error (MAE) and R2 of examination step of this scenario were 0.09, 0.07 mm/day and 0.9, respectively. Overall, the statistic performances revealed that ANN and ANFIS had the same results and similar input layer sensitivity. The iteration times of the ANN and ANFIS methods to reach the best results were 26 and 40, respectively. Comparison between ANN (RMSE= 0.09 mm/day) and standard Penman-Montieth method (RMSE= 0.34 mm/day) confirmed that the intelligence approaches such as ANN are more accurate for reference evapotranspiration estimation.
Keywords: Reference evapotranspiration, Pierson test, Intelligence methods, Hamedan
H. Zreabyaneh; A. Ghasemi; M. Bayat; S. Marofi
Abstract
Abstract
Evapotranspiration as one of the important elements in agriculture has a considerable role in water resource management. Therefore, using a more exact estimation method is an essential step of agricultural development, especially in arid semi-arid area. In this research, in order to exact estimate ...
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Abstract
Evapotranspiration as one of the important elements in agriculture has a considerable role in water resource management. Therefore, using a more exact estimation method is an essential step of agricultural development, especially in arid semi-arid area. In this research, in order to exact estimate of garlic evapotranspiration using lysimeteric data, an artificial neural network (ANN) model was developed. Maximum and minimum air temperatures, maximum and minimum relative humidity values, wind speed and sunshine hours were used as the input layer data. The crop evapotranspiration was measured using 4 lysimetres of 2×2×2m of the Bu-Ali Sina agriculture collage’s meteorology station during 2006-2008. Statistic indicators RMSE, MAE, STDMAE R2 were used for performance evaluation of the models. The results showed the more exact method concerned to the multilayer perceptron (MLP) model with the back propagation algorithm. The 6-6-1 layout with Levenberg-Marquat rule and sigmoid function had the best topology of the model. The evaluation criteria were 0.088, 0.07 and 0.061 mm/day as well as 0.88, respectively. The results also showed that the average daily garlic evapotranspiration were 8.3 and 6.5 mm based on the lysimeter ANN methods, respectively. Overall, evaluation of ANN results showed that the errors of ANN were negligible. The ANN showed high and low sensitivity to maximum air temperature and minimum relative humidity, respectively.
Key words: Artificial Neural Networks, Evapotranspiration, Lysimeter, Garlic, Hamedan
