DSpaceCRIS@UNITENhttp://dspace.uniten.edu.my/jspuiThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 19 Sep 2020 02:44:51 GMT2020-09-19T02:44:51Z5041- Toward bridging future irrigation deficits utilizing the shark algorithm integrated with a climate change modelhttp://dspace.uniten.edu.my/jspui/handle/123456789/12825Title: Toward bridging future irrigation deficits utilizing the shark algorithm integrated with a climate change model
Authors: Ehteram, M.; El-Shafie, A.H.; Hin, L.S.; Othman, F.; Koting, S.; Karami, H.; Mousavi, S.-F.; Farzin, S.; Ahmed, A.N.; Zawawi, M.H.B.; Hossain, M.S.; Mohd, N.S.; Afan, H.A.; El-Shafie, A.
Abstract: Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall-runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam. © 2019 by the authors.
Tue, 01 Jan 2019 00:00:00 GMThttp://dspace.uniten.edu.my/jspui/handle/123456789/128252019-01-01T00:00:00Z
- An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspirationhttp://dspace.uniten.edu.my/jspui/handle/123456789/13039Title: An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration
Authors: Ehteram, M.; Singh, V.P.; Ferdowsi, A.; Mousavi, S.F.; Farzin, S.; Karami, H.; Mohd, N.S.; Afan, H.A.; Lai, S.H.; Kisi, O.; Malek, M.A.; Ahmed, A.N.; El-Shafie, A.
Abstract: Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models. © 2019 Ehteram et al.
Tue, 01 Jan 2019 00:00:00 GMThttp://dspace.uniten.edu.my/jspui/handle/123456789/130392019-01-01T00:00:00Z
- Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencieshttp://dspace.uniten.edu.my/jspui/handle/123456789/13099Title: Development of a novel hybrid optimization algorithm for minimizing irrigation deficiencies
Authors: Valikhan-Anaraki, M.; Mousavi, S.-F.; Farzin, S.; Karami, H.; Ehteram, M.; Kisi, O.; Fai, C.M.; Hossain, M.S.; Hayder, G.; Ahmed, A.N.; El-Shafie, A.H.; Bin Hashim, H.; Afan, H.A.; Lai, S.H.; El-Shafie, A.
Abstract: One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991-2000) was 25.12 × 106 m3, while the amount of water release based on the HA was 24.48 × 106 m3. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands. © 2019 by the authors.
Fri, 01 Nov 2019 00:00:00 GMThttp://dspace.uniten.edu.my/jspui/handle/123456789/130992019-11-01T00:00:00Z
- Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation predictionhttp://dspace.uniten.edu.my/jspui/handle/123456789/13052Title: Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction
Authors: Ghazvinian, H.; Mousavi, S.-F.; Karami, H.; Farzin, S.; Ehteram, M.; Hossain, M.S.; Fai, C.M.; Hashim, H.B.; Singh, V.P.; Ros, F.C.; Ahmed, A.N.; Afan, H.A.; Lai, S.H.; El-Shafie, A.
Abstract: Solar energy is a major type of renewable energy, and its estimation is important for decision- makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS. © 2019 Ghazvinian et al.
Wed, 01 May 2019 00:00:00 GMThttp://dspace.uniten.edu.my/jspui/handle/123456789/130522019-05-01T00:00:00Z