4 results
Search Results
2. Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.
- Author
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Banadkooki, Fatemeh Barzegari, Ehteram, Mohammad, Ahmed, Ali Najah, Teo, Fang Yenn, Ebrahimi, Mahboube, Fai, Chow Ming, Huang, Yuk Feng, and El-Shafie, Ahmed
- Subjects
SUSPENDED sediments ,MATHEMATICAL optimization ,FORECASTING ,ALGORITHMS ,ARTIFICIAL neural networks - Abstract
Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
3. Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies.
- Author
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Karami, Hojat, Farzin, Saeed, Jahangiri, Aylin, Ehteram, Mohammad, Kisi, Ozgur, and El-Shafie, Ahmed
- Subjects
PARTICLE swarm optimization ,MATHEMATICAL optimization ,WATER supply ,WATER shortages ,SEARCH algorithms ,ALGORITHMS - Abstract
The growing prevalence of droughts and water scarcity have increased the importance of operating dam and reservoir systems efficiently. Several methods based on algorithms have been developed in recent years in a bid to optimize water release operation policy, in order to overcome or minimize the impact of droughts. However, all of these algorithms suffer from some weaknesses or drawbacks – notably early convergence, a low rate of convergence, or trapping in local optimizations – that limit their effectiveness and efficiency in seeking to determine the global optima for the operation of water systems. Against this background, the present study seeks to introduce and test a Hybrid Algorithm (HA) which integrates the Gravitational Search Algorithm (GSA) with the Particle Swarm Optimization Algorithm (PSOA) with the goal of minimizing irrigation deficiencies in a multi-reservoir system. The proposed algorithm was tested for a specific important multi-reservoir system in Iran: namely the Golestan Dam and Voshmgir Dam system. The results showed that applying the HA could reduce average irrigation deficiencies for the Golestan Dam substantially, to only 2 million cubic meters (MCM), compared to deficiency values for the Genetic Algorithm (GA), PSOA and GSA of 15.1, 6.7 and 5.8 MCM respectively. In addition, the HA performed very efficiently, reducing substantially the computational time needed to achieve the global optimal when compared with the other algorithms tested. Furthermore, the HA showed itself capable of assuring a high volumetric reliability index (VRI) to meet the pattern of water demand downstream from the dams, as well as clearly outperforming the other algorithms on other important indices. In conclusion, the proposed HA seems to offer considerable potential as an optimizer for dam and reservoir operations world-wide. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
4. A Novel Approach to the Gas-Lift Allocation Optimization Problem.
- Author
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Hamedi, H., Rashidi, F., and Khamehchi, E.
- Subjects
OIL well gas lift ,MATHEMATICAL optimization ,PARTICLE swarm optimization ,ALGORITHMS ,OIL fields ,PERFORMANCE - Abstract
Because compressed gas is a scarce and expensive resource, allocating an optimal amount of gas injection to a group of wells to increase the oil production rate is an important optimization problem in the gas lift operation. In this article, a particle swarm optimization algorithm is employed to assign an optimum gas injection rate for each individual well. Also, a new gas lift performance curve-fit that can reduce the time and volume of the computation is suggested. Finally, the algorithm is tested on five wells in an Iranian oil field. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
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