Hamidreza Babaali; Reza Dehghani
Volume 4, Issue 11 , September 2017, , Pages 149-168
Abstract
Introduction
Flood is one of the hazardous natural disasters that causes loss of life and financial problems every year. Therefore, scientists have tried to assess the quantitative variability of this phenomenon as much as possible. In this study, the recorded data in Kahman Aleshtar watershed area, ...
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Introduction
Flood is one of the hazardous natural disasters that causes loss of life and financial problems every year. Therefore, scientists have tried to assess the quantitative variability of this phenomenon as much as possible. In this study, the recorded data in Kahman Aleshtar watershed area, which is located in Lorestan province, was used to investigate the precision of the different flood peak discharge prediction models. In addition, the wavelet and artificial neural network models were selected for the modeling of the flood peak discharge and the results were compared to examine the accuracy of the studied models.
Methodlogy
Daily flood peak discharges of the basin in Kahman station, which were applied for the calibration and validation of the models, were selected and observed. For this purpose, maximum daily precipitation rate, at a daily scale and between the years 2001-2012, and flood peak discharge were respectively used as the input and output parameters. The wavelet-based neural network which was based on the combination of the wavelet theory and neural networks were created. Indeed, it has the benefits and features of the neural networks and the charm, flexibility, strong mathematical foundations, and the analysis of the multi-scale wavelets. The combination of the wavelet theory with the neural network concepts for the creation of the wavelet neural network and feed-forward neural shock can be a good alternative for estimating the approximate nonlinear functions. Feed-forward neural network with sigmoid activation function is in the hidden layer. While at the nerve shocked wavelet, the wavelet functions as the activation of the hidden layer feed-forward networks are considered, in both these networks and scale wavelet, the transformation parameters are optimized with their weight. Artificial neural networks inspired by the brain's information processing systems, designed and emerged into. To help the learning process and with the use of the processors called neurons, there was an attempt to understand the inherent relationships among the data mapping, the input space, and the optimal space. The hidden layer or layers, the information received from the input layer, and the output layer are the processing and disposal.
Based on the artificial neural network structure, its major features are high processing speed and the ability to learn the pattern, the ability to extend the model after learning, the flexibility against unwanted errors, and no disruption to the error on the part of the connection due to the weight distribution network.
The first practical application of the synthetic networks with the introduction of the multilayer perceptron networks was consultation. For training this network, back propagation algorithm is used. The basis of this algorithm is based on the error correction of the learning rule. That consists of two main routes. By adjusting the parameters in the MLP model, error signal and input signal occurs. Determining the number of the layers and neurons is the most important issue in simulation with the artificial neural network. The criteria of the correlation coefficient, the root mean square error, and the mean absolute error were used to evaluate and compare the performance of the models.
Results
The results showed that both models in a structure, consisting of 1 to 4 delay, gives better results than any other structure. In addition, based on the results of the evaluation criterion, the model which was used to wavelet neural network model, was the most accurate (R=0.921), and the lowest root mean square error RMSE=0.005m3/s and the lowest average absolute error MAE=0.003m3/s the validation phase is capable.
Conclusions
Wavelet neural network model outperformed the artificial neural network. Consequently, it can be effective in forecasting the daily flood peak discharge. It can also facilitate the development and the implementation of the surface water management strategies. Finally, predicting the piver flow process is a major step in water engineering studies and water resources' management.
Ata Allah Nadiri; Saeed Yousefzadeh
Volume 4, Issue 10 , June 2017, , Pages 21-40
Abstract
An accurate estimation of the hydrogeological parameters such as hydraulic conductivity, which is essential for careful management and protection of groundwater resources, is an important part of hydrogeological studies. Various field and laboratory methods, generally done using hydrogeological data, ...
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An accurate estimation of the hydrogeological parameters such as hydraulic conductivity, which is essential for careful management and protection of groundwater resources, is an important part of hydrogeological studies. Various field and laboratory methods, generally done using hydrogeological data, have already been proposed for estimating hydraulic conductivity. One of the best and the most complete methods is the field pumping test which is very time-consuming and expensive. In addition, hydrogeological parameters estimated by it have an inherent uncertainty. In this study, we tried to use artificial intelligence methods, widely considered in recent years, such as artificial neural network (ANN), mamdani fuzzy logic(MFL), sugeno fuzzy logic(SFL), and adoptive neuro-fuzzy inference system (ANFIS) for the estimation of the hydraulic conductivity. In this study, for the accurate estimation of the hydraulic conductivity in Maraghe-Bonab plain by these models, geophysical and hydrogeological data were used as models' inputs. Their results were compared with the evaluation criteria, and the best model based on the RMSE was selected. Accordingly, the ANFIS model, compared to other models, with an RMSE of 1.12 in the test phase has high power in the estimation of the hydraulic conductivity. Radius of clustering, number of fuzzy rules, and number of clusters are very important in fuzzy and neuro-fuzzy models. Radius of clustering in the ANFIS model, based on the minimum RMSE amount, was equal to 0.4 and the numbers of clusters, based on if-then fuzzy rules, was 9. The methods presented in this study, which demonstrated superior performance in estimating hydraulic conductivity of Maragheh-Bonab plain, can be used in estimating hydraulic conductivity of other plains with similar hydrogeological conditions.
Taher Rajayee; Fatemeh Pouraslan
Volume 2, Issue 4 , January 2017, , Pages 1-19
Abstract
Taher Rajayee[1]* Fatemeh Pouraslan[2] Abstract In this article, a hybrid, artificial neural network-geostatistics (Kriging) methodology is utilized to predict the spatiotemporal groundwater level in Davarzan plain in Khorasan Razavi province in Iran. The data for the study were the groundwater levels ...
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Taher Rajayee[1]* Fatemeh Pouraslan[2] Abstract In this article, a hybrid, artificial neural network-geostatistics (Kriging) methodology is utilized to predict the spatiotemporal groundwater level in Davarzan plain in Khorasan Razavi province in Iran. The data for the study were the groundwater levels of 5 piezometers from September 2003 to April 2012 which were recorded on monthly basis. Neural network was used for predict the groundwater level of the successive months and geostatistic were used to estimate the groundwater level at any desired point in the plain. To determine the accuracy and efficiency of model, the method was tested on a new piezometer (Bagherabad) at the first stage. The results were compared with the actual value. And the results (E=0.812) show the efficiency of model. Then, based on appropriate achieved results, the groundwater level was predicted in the month ahead. The results show that neural network with average coefficient of determination (E=0.688) and Gaussian variogram with (R2=0.657) had high efficiency for predicting the groundwater level in this plain. [1]- Assistant Prof., Dept., of Civil Eng.; University of Qom; Iran (Corresponding author), Email:taher_rajaee@yahoo.com. [2]- M.A Student; Hydraulic Structures; University of Qom; Iran.