Extracting the Optimal Role Curve of Dams in Real Time based on the Integration of Meta-Exploration Algorithm and Machine Learning Technique

Document Type : Research Paper

Authors

1 Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran

2 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

Abstract

The use of coupled simulation-optimization models to extract the optimal role curve of dams is one of the effective strategies for optimal management of reservoirs. In certain optimization techniques, historical data of the inflow to the reservoir is usually used to extract the optimal role curve of the dam. It is possible that in the coming years, with the change of the inflow to dams, the parameters based on which the optimal role curve was extracted may no longer work and the results may be unexpected. The objective of this research is to provide a solution for extracting the optimal role curve in real time so that by changing the inflow to the dam in the future without re-executing the optimization algorithm and using artificial intelligence techniques in the shortest time, the optimal role curve compatible with the new conditions can be obtained. In this research, the integration of the NSGA-II multi-objective algorithm and the WEAP simulation model is used to derive optimal policies based on historical data. Then, using the support vector machine method and the results obtained from the output of the optimization algorithm, a new structure is developed so that the optimal role curve can be obtained in real time and based on new inputs. The results indicate that the average error of the optimal role curve extracted from support vector machines is less than 2.5% compared to the role curve obtained from the NSGA-II algorithm in the calibration and validation stages. Therefore, the developed support vector machine model has the ability to quickly provide optimal operation policies in such a way that provides the possibility of optimal management of the system in real time, according to the new data of the inflow to the dam.

Keywords

Main Subjects

EXTENDED ABSTRACT

Introduction

In the deterministic optimization method, a historical series of inflow to the reservoir through the operation time is considered and the release volume from the reservoir is optimized to provide downstream consumption in these conditions. The disadvantage of such models is that the optimal solutions cannot be generalized to other possible inflows to the reservoir and if the inflow to the reservoirs changes, the optimal solutions will no longer work and the system must be operated in the form of an optimizer algorithm. Thus, the main objective of this research is to integrate the support vector machine model with the NSGA-II optimization algorithm for optimal real-time operation from the system.

Materials and methods

This study utilizes an integration of the NSGA-II multi-objective algorithm and WEAP simulator model so that the first objective is to maximize the reliability of providing the needs in front of the second goal, i.e., to minimize water table drawdown at the end of the operation time. The dam role curve or the amount of released volume from the reservoir is optimized to supply downstream uses in these conditions. However, in certain optimizations, the optimal solutions cannot be generalized to other possible inputs to the reservoir, and if the inflow to the reservoirs changes, the obtained optimal solutions are no longer efficient and the system must be re-optimized in the form of an optimizer algorithm. Therefore, to solve this problem, a new method is extended on the basis of the combination of the support vector machine and NSGA-II algorithm for optimal real-time operation of the system. In this case, after completing the algorithm and extracting the optimal variables, there will be a relationship between the monthly inflows to the reservoir, water storage volume in the reservoir, reservoir volume changes and downstream needs (as independent parameters) and the optimal release rate variable (as a dependent parameter). This means that in each future simulation period, by determining the first four parameters at the beginning of each month, the optimal real-time release volume will be determined.

Result and discussion

The results demonstrate that the average error rate of optimal rules derived from support vector machines is less than 2.5% compared to the output of the NSGA-II algorithm in the verification step, which indicates the efficiency of this method in predicting the optimal pattern of the dam role curve in real-time. In this structure, based on the inflow to the reservoir, the volume of water storage in the reservoir and changes in the reservoir storage (at the beginning of the month) and the downstream demands of the current month, the optimal release amount can be achieved in real-time. Therefore, the developed support vector machine has the ability to update the rule curve of the dam based on the new data if the input flow to the dam changes and provide the possibility of operating the system in real time. In this structure, unlike the common structure of deterministic optimization, if the inflow changes, it is no need to re-optimize to understand the optimal coefficients, however by utilizing the relationship obtained from the support machine method it is possible to predict the release volume in real-time on the basis of the inflow to the reservoir (measured at the first day of the month), the volume of water storage in the reservoir (measured at the first day of the month) and changes in the reservoir storage and downstream needs in the current month.

Conclusion

In general, the results showed that the SVM-NSGA-II developed model has good capability and efficiency in solving complex and completely nonlinear problems and providing optimal solutions based on 24 answers on the Pareto optimal front. Among these solutions, according to the valuation of the objective functions, the solution that had the lowest groundwater drop function and the highest percentage of supplying the demands was selected as the superior answer. The results of applying the optimal release values from the dam (optimal role curve of the dam) showed that considering the optimal policy, the percentage of providing and reliability of supplying the most demands is appropriate and acceptable. The developed SVM-NSGA-II model has the ability to provide optimal operation policies based on new data of the inflow to the dam in a way that allows us to optimally manage the system in real-time.

 

 

References

Azari, A., Arman, A. (2020). Optimal Utilization of Water Resources in Real Time Based on NSGA-II Algorithms and Support Vector Machines (Case Study: Gavoshan Dam). Irrigation Sciences and Engineering (JISE), 43(1), 189-204.
Azari, A., Hamzeh, S., & Naderi, S. (2018). Multi-objective optimization of the reservoir system operation by using the hedging policy. Water Resources. Management, 32(6), 2061–2078.
Azari, A., Zeynoddin, M., Ebtehaj, I., Sattar, A. M. A., Gharabaghi, B. and Bonakdari, H. 2021. Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting. Acta Geophysica, 69, 1395–1411. https://doi.org/10.1007/s11600-021-00617-2.
Bayesteh, M., & Azari, A. (2021). Stochastic Optimization of Reservoir Operation by Applying Hedging Rules. Journal of Water Resources Planning and Management, 147(2), 04020099.
Blum, C., & Roli, A. (2003). Metaheuristics in combinational optimization: Overview and conceptual comparision. ACM Computing Surveys, 35(3), 268-308.
Candela, J. Q., & Hansen, L. K. (2002). Time series prediction based on the Relevance Vector Machine with adaptive kernels," Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference, 985-988.
Chang, J. F., Chen, L., & Chang, C. L. (2005). Optimizing reservoir operating rule curves by genetic algorithms. Hydrological Processes, 19, 2277-2289.
Deb, k., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evolutionary Computing, Indian, 6(2), 182–197.
Du, J., Liu, Y., Yu, Y., & Yan, W. (2017). A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms, Algorithms, 10(57), 1-15.
Fatemi, S. E., Parvini, H. 2022. The impact assessments of the ACF shape on time series forecasting by the ANFIS model, Neural Computing and Applications. 34 (15): 12723–12736. https://doi.org/10.1007/s00521-022-07140-5.
Goorani, Z., & Shabanlou, S. (2021). Multi-objective optimization of quantitative-qualitative operation of water resources systems with approach of supplying environmental demands of Shadegan Wetland, Journal of Environmental Management, 292(6), 112769.
Huang, W., Liu, H., Zhang, Y., Mi, R., Tong, C., Xiao, W., Shuai, B. 2021. Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM, Applied Soft Computing. 109: 107541. https://doi.org/10.1016/j.asoc.2021.107541.
Jalilian, A., Heydari, M., Azari, A. and Shabanlou, S. (2022). Extracting Optimal Rule Curve of Dam Reservoir Base on Stochastic Inflow. Water Resources Management. 36 (6): 1763–1782. https://doi.org/10.1007/s11269-022-03087-3.
Jian, C., Qiang, H., & Min, W. (2005(. Genetic algorithm for optimal dispatchin. Water Resource Planning and Management, 19, 321-331.
Kalita, H. M., Sarma, A. K., & Bhattacharjya, P. K. (2007). Evaluation of Optimal River Training Work Using GA Based Linked Simulation-Optimization Approach. Water Resources Management, 28, 2077–2092.
Karamian, F., Mirakzadeh, A. A., Azari, A. 2023. Application of multi-objective genetic algorithm for optimal combination of resources to achieve sustainable agriculture based on the water-energy-food nexus framework. Science of The Total Environment. 860: 160419. https://doi.org/10.1016/j.scitotenv.2022.160419.
Lei, J., Quan, Q., Li, P., & Yan, D. (2021). Research on Monthly Precipitation Prediction Based on the Least Square Support Vector Machine with Multi-Factor Integration. Atmosphere, 12(8), 1076.
Lin, J. Y., Cheng, C. T., & Chau, K. W. (2006). Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal, 51(4), 599-612.
Momtahen, Sh., & Dariane, A. B. (2007). Direct search approaches using genetic algorithms for optimization of water reservoir operating policies. Water Resource Planning and Management, 133(3), 202–209.
Nicklow, J., Reed, P., Savic, D., Dessalegne, T., Harrell, L., Chan-Hilton, A., Karamouz, M., Minsker, B., Ostfeld, A., Singh, A., & Zechman, E. (2010). State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management, Journal of Water Resources Planning and Management, 136, 412-432.
Smola, A. J., Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing. 14 (3): 199–222. https://doi.org/10.1023/B%3ASTCO.0000035301.49549.88.
Soltani., K., and Azari, A. (2022). Forecasting groundwater anomaly in the future using satellite information and machine learning. Journal of Hydrology, 612 (2): 128052. https://doi.org/10.1016/j.jhydrol.2022.128052.
Soltani, K., Ebtehaj, I., Amiri, A., Azari, A., Gharabaghi, B. and Bonakdari, H. 2021. Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of The Total Environment, 770, 145288. https://doi.org/10.1016/j.scitotenv.2021.145288.
Su, J., Wang, X., Liang, Y., & Chen, B. (2014). GA-Based Support Vector Machine Model for the Prediction of Monthly Reservoir Storage. Journal of Hydrologic Engineering, 19(7), 1430-1437.
Tennant, D. L. (1976). Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries, 1(4), 6-10.
Thissen, U., van Brakel, R., de Weijer, A. P., Melssen, W.J., & Buydens, L. M. C. (2003). Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems, 69, 35–49.
Vapnik, V. N., and Cortes, C. 1995. Support vector networks. Machine Learning, 20, 273-297.
Wardlaw, R., & Sharif, M. (1999). Evaluation of genetic algorithms for optimal reservoir system operation. Water Resource Planning and Management, 125(1), 25-33.
Xi, X. C., Poo, A. N., & S. K. Cho. (2007). Support vector regression model predictive control on a HVAC plant. Control Engineering Practice, 15, 897–908.
Yadav, A., Joshi, D., Melingi, S. B., Revanth, J., Aashrith, P., Chithaluru, P. (2022). Integration of genetic algorithm and support vector machine for water discharge prediction in river basin system. NeuroQuantology, 20 (10), 11811-11821. https://doi.org/ 10.14704/NQ.2022.20.10.NQ551145.
Zarei, N., Azari, A., & Heidari, M. M. (2022). Improvement of the performance of NSGA-II and MOPSO algorithms in multi-objective optimization of urban water distribution networks based on modification of decision space. Applied Water Science, 12(6), 133. https://doi.org/10.1007/s13201-022-01610-w.
Zeinali, M., Azari, A., & Heidari, M. (2020b). Multiobjective Optimization for Water Resource Management in Low-Flow Areas Based on a Coupled Surface Water–Groundwater Model. Journal of Water Resource Planning and Management, 146(5), 04020020.
Zhang, W., Hou, S., Yin, H., Li, L., Wu, K. (2022). Evaluation of Regional Water-Saving Level Based on Support Vector Machine Optimized by Genetic Algorithm. Water, 14 (17), 2615. https://doi.org/10.3390/w14172615.
Iranian Journal of Soil and Water Research
Volume 54, Issue 4
July 2023
Pages 695-712

Files

Share

How to cite

Statistics