Geomorphology
Saeid Roustami; Babak Shahinejad; Hojatolah Younesi; Hassan Torabipoudeh; Reza Dehghani
Abstract
Flood is one of the natural phenomena that causes a lot of human and financial losses in the world every year and creates many problems for the economic and social development of countries. Therefore, in order to reduce the damage, control and guidance of this phenomenon, estimating flood discharge and ...
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Flood is one of the natural phenomena that causes a lot of human and financial losses in the world every year and creates many problems for the economic and social development of countries. Therefore, in order to reduce the damage, control and guidance of this phenomenon, estimating flood discharge and identifying the factors affecting it is very important. In this study, in order to estimate the flood discharge of Kashkan catchment located in Lorestan province, new hybrid artificial intelligence models including artificial neural network - innovative gunner, artificial neural network - black widow spider and artificial neural network - chicken crowding during the period 1300-1400 were used. To evaluate the simulation performance, statistical indices of determination coefficient (R2), absolute mean error (MAE), Nash-Sutcliffe productivity coefficient (NSE), bias percentage (PBIAS) were used. The results showed that hybrid artificial intelligence models improve the performance of the single model. The results showed that the artificial neural network- innovative gunner model has more accuracy and less error than other models. Overall, the results showed that the use of hybrid artificial intelligence models is effective in estimating flood discharge and can be considered as a suitable and rapid solution in water resources management.
reza dehghani; hassan torabi; hojatolah younesi; babak shahinejad
Abstract
River flow prediction is one of the most important key issues in the management and planning of water resources, in particular the adoption of proper decisions in the event of floods and the occurrence of droughts. In order to predict the flow rate of rivers, various approaches have been introduced in ...
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River flow prediction is one of the most important key issues in the management and planning of water resources, in particular the adoption of proper decisions in the event of floods and the occurrence of droughts. In order to predict the flow rate of rivers, various approaches have been introduced in hydrology, in which intelligent models are the most important ones. In this study the application of hybrid wavelet vector hybrid model to estimate the discharge of Kharkhe basin rivers on daily discharge statistics of hydrometric stations located upstream of dam during the statistical period (2008-2018) has been evaluated and its performance with vector machine model The backup was compared. The correlation coefficients, root mean square error, mean absolute error was used for evaluation and also comparison of the performance of models in this research. The results showed that the hybrid structures presented acceptable results in the modeling of river discharge. Comparison of models also showed that the hybrid model of support-wavelet vector machine has better performance in flow forecasting. .Overall, the results showed that using a hybrid backup vector machine model can be useful in predicting daily discharge.
hassan torabipodeh; Babak Shahinejad; Reza Dehghani
Volume 5, Issue 14 , June 2018, , Pages 179-197
Abstract
Background and Objective
Drought is one of the phenomena of climate that occurs in all climatic conditions and in all parts of the planet. Drought prediction has an important role in designing and managing natural resources, water resource systems, and determining the plant's water requirement. For ...
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Background and Objective
Drought is one of the phenomena of climate that occurs in all climatic conditions and in all parts of the planet. Drought prediction has an important role in designing and managing natural resources, water resource systems, and determining the plant's water requirement. For estimating drought, various approaches have been introduced in hydrology that artificial models are the most important ones. In this study for evaluating the accuracy of the models in estimating the 12-month standard rainfall index, monthly data from four weather stations in Boroujerd, Dorood, Selseleh and Dolphan in Lorestan province have been used. For modeling of drought in these stations utilized wavelet neural network and artificial neural network models and the results were compared to each other for the accuracy of the studied models. In a few studies, each of the models presented in the drought estimation has been studied. But the purpose of this research is simultaneous analysis of these models at four stations for estimating the standard rainfall index.
Methods
In this study, Boroujerd, Dorood, Selseleh and Dolphan that located in Lorestan province have been selected as the study area During the statistical period, the precipitation parameter was used at monthly time scale (1962-1372) for input and standard rainfall index as the output parameter of the models. For this purpose, at first 80% of the data (1372-1382) were selected for calibration of the models and 20% of the data (2012-2013) were used to validate the models. The wavelet neural network, which has a very good fit with the sinusoidal equations by separating the signal into high and low frequencies, can greatly increase the accuracy of the model and reduce noise. Artificial neural networks are inspired by the brain information processing system that ability to approximate patterns of a model has increased the scope of these networks. Correlation coefficient, root mean square error and mean absolute error value were used for evaluation and performance of the models.
Results
The results showed that both models have good performance in estimating the standard rainfall index in the four stations studied. Also, according to the evaluation criteria, the wavelet neural network model was found to have the highest accuracy and low error rate compared to the artificial neural network model.
Conclusions
In total, the results showed that the use of wavelet neural network model can be effective in estimating the standard rainfall index. also It can be useful in facilitating the development and implementation of management strategies to prevent drought and is a step in making managerial decisions to improve water resources.
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.
