Ali Azareh; Elham Rafiei Sardooi
Abstract
The purpose of this study is to investigate land use changes in the past and predict future land use using land change modeler in Halil River watershed. The detection of land use changes was performed using Landsat satellite images (L5-TM-1991, L7- ETM+-2003 and L8-OLI-2020). Transition potential modeling ...
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The purpose of this study is to investigate land use changes in the past and predict future land use using land change modeler in Halil River watershed. The detection of land use changes was performed using Landsat satellite images (L5-TM-1991, L7- ETM+-2003 and L8-OLI-2020). Transition potential modeling was done using MLP neural network method and eight variables including altitude, slope, aspect, distance to road, distance to river, distance to agricultural lands, distance to urban and Normalized Difference Vegetation Index (NDVI). Finally, the Markov chain was used to predict future land use changes. Investigating the calibration periods using kappa statistics showed that the period of 1991-2020 had the highest accuracy to predict land use for 2041. The results of land use changes indicated that during the calibration period, among the six categories namely rangeland, agricultural land, residential land, barren land, rock and orchard, the highest increase and the highest decrease in area was related to agricultural lands and rangelands by 293.7 and 382.6 km2, respectively. Also, the area of barren lands, orchard and residential lands has increased and rocky lands have remained unchanged. The degradation of rangelands has been more in line with the conversion of these lands into agricultural, orchard and residential lands. Also, the prediction of future land use map (2041) using land change modeler showed that , the area of rangelands will decrease by 201.1 km2 and the area of agricultural lands, residential lands, orchards and barren lands will increase by 158.01, 22.38, 20.2 and 0.53 km2, respectively.
Mojtaba Nassaji zavareh; Bagher Ghermezcheshmeh; Fatemeh Rahimzadeh
Abstract
Daily constant discharges are needed estimating daily discharge in the hydrological model. The different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. Hence the reconstruction of daily discharge data is of ...
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Daily constant discharges are needed estimating daily discharge in the hydrological model. The different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. Hence the reconstruction of daily discharge data is of paramount importance. In this research, daily discharge was reconstructed in two stages in one of the upstream of Karoun River. In both stages of research, daily discharge data from two upstream stations were used to reconstruct daily discharge of the downstream station using artificial neural networks, neuro-fuzzy and two variables regression methods. In the second stage, the magnitudes of discharge, based on dry, normal and wet years was used to reconstruct the daily discharge. The results showed higher accuracy in the artificial neural network and neuro-fuzzy methods compared to two variable regression methods in the reconstruction of daily discharge. Multi-layer perceptron model has better potential among all different method of artificial neural network and neuro-fuzzy models. Classification of discharge into dry, normal, and wet years decreases error in the reconstruction of daily discharge. Based on the mean relative error (MRE), error in reconstruction of daily discharge is the least in normal, wet, and dry years, respectively
Amin Zoratipour
Abstract
Abstract
Estimation of fine suspended load rivers is important in designing reserves, transition volume ofsediment, and estimating lake pollution. Thus, some methods are needed for determining damagescaused by sedimentations in environment and determining its effects on the watersheds. There aremany ...
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Abstract
Estimation of fine suspended load rivers is important in designing reserves, transition volume ofsediment, and estimating lake pollution. Thus, some methods are needed for determining damagescaused by sedimentations in environment and determining its effects on the watersheds. There aremany methods for estimating suspended load, one of these methods that solves the problems ofsediment discharge and can predict it is using Neuro fuzzy or ANFIS (Adaptive Network FuzzyInference System), and ANN (Artificial Neural Network) methods. These make a function betweensediment and simultaneous discharge by use of different algorithms. The goal of this research iscomparing the effectiveness of Neuro fuzzy, neural network artificial and statistical methods forestimating suspended load river in Glinak station of Taleghan Basin. It was found out thatsuspended load estimations of Nero fuzzy method with MAE 1006 ton/day, and correlationefficiency (R) 77%, RMSE 2621 ton/day and Nash-Sutcliff error (NS) 0.51 is better than NeuralNetwork Artificial and Statistical methods and Artificial Neural Network method rather thanStatistical Method are more proper. Also, contracting both neural networks artificial to fuzzy lawscan be illustrated better than other methods, variation of sediment Load River. One more merit ofthis method is that it is not sensitive to few errors in early statistical data and this fact enables betterestimation of neural network model in comparison with statistical model. Finally, Neuro fuzzymethod works better as the percent of train data to test data increases.
Maryam Khosravi; Ali Salajegheh; Mohammad Mahdavi; Mohsen Mohseni Saravi
Abstract
It is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. Hence, at present study, two models including Artificial Neural Networks and nonlinear multivariate regression were used to predict peak discharge in Taleghan ...
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It is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. Hence, at present study, two models including Artificial Neural Networks and nonlinear multivariate regression were used to predict peak discharge in Taleghan watershed. Maximum daily mean discharge and corresponding daily rainfall, one day antecedent and five days antecedent rainfall, sum of five days antecedent rainfall and monthly mean temperature were extracted in Gatehdeh, Mehran, Alizan, Joestan and Gelinak hydrological units and entered into neural network model (from upstream to downstream, respectively). The feed forward network was used with one hidden layer and back-propagation algorithm. Then, the models were trained, validated and tested in three stages. The observed and estimated peak discharges of the models were compared based on RMSE and r. The results showed that neural network has better performance than nonlinear multivariate regression.
A. Salajegheh; A. Fathabadi; M. Mahdavi
Volume 62, Issue 1 , June 2009
Abstract
Rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. In this study statistical method ARMAX model, neural network, neuro-fuzzy (ANFIS subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate ...
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Rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. In this study statistical method ARMAX model, neural network, neuro-fuzzy (ANFIS subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate rainfall-runoff and prediction of streamflow. In each method optimum structure was determined then, streamflow forecasted using the best model. The results showed that hybrid methods have better application than single models and artificial intelligent has better application than linear ARMAX model due to nonlinearity of rainfall-runoff process. In this study all methods showed relatively suitable application but ANFIS method with subtractive clustering is suggested for modeling rainfall-runoff and streamflow prediction.
