Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves
Abstract
:1. Introduction
2. Inflow Data
3. Objective Function
3.1. The Shortage Index
3.2. The Average Water Shortage
3.3. The Highest Water Shortage
3.4. The Frequency of Water Shortage
3.5. The Total Square Deficit
3.6. The Combination of Water Shortage Terms (SUM)
4. Smoothing Function
- Initialize the rule curves based on the desired smoothing pattern.
- Simulate the reservoir operations using the rule curves.
- Calculate the fitness of the simulated rule curves by considering multiple objective functions, including the smoothing function constraints.
- Adjust the rule curves using an optimization algorithm to improve the fitness.
- Repeat steps 2–4 until the desired fitness criteria are met.
- Obtain the final optimized rule curves that satisfy the smoothing function constraints and provide optimal reservoir operation.
- -
- x1, y1: Beginning level in January (early drought season)
- -
- x5, y5: Level in May
- -
- x6, y6: Beginning level in June (end of drought season, beginning of flood season, and start of the lower rule curve for the flood season)
- -
- x12, y12: Level in December (end of the upper rule curve for the flood season)
5. Downstream Water Demand
6. Initial Condition of Reservoir Characteristic
7. Evaluation Scenarios
8. Reservoir Size
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Smith, A.B.; Johnson, C.D.; Brown, E.A. Water resource problems, population growth, and economic expansion. Water 2018, 10, 1257. [Google Scholar]
- Kangrang, A.; Prasanchum, H.; Sriworamas, K.; Ashrafi, S.M.; Hormwichian, R.; Techarungruengsakul, R.; Ngamsert, R. Application of optimization techniques for searching optimal reservoir rule curves: A Review. Water 2023, 15, 1669. [Google Scholar] [CrossRef]
- Huang, X.; Wang, L.; Zhang, X. Balancing supply and demand: A comprehensive approach to water resource management. Water 2019, 11, 684. [Google Scholar]
- Chen, Y.; Li, H.; Liu, H. Financial considerations in water resource management: A study on the construction of hydraulic control structures. Water 2017, 9, 896. [Google Scholar]
- Wang, S.; Zhou, J.; Zhu, Y. Non-construction measures in water resource management: Improving irrigation efficiency, changing crop patterns, and optimizing reservoir operation. Water 2016, 8, 425. [Google Scholar]
- Li, J.; Zhang, Q. Rule curves in reservoir operation: Determining water release based on long-term demand. Water 2018, 10, 1653. [Google Scholar] [CrossRef] [Green Version]
- Kosasaeng, S.; Kangrang, A. Optimum reservoir operation of a networking reservoirs system using conditional atom search optimization and a conditional genetic algorithm. Heliyon 2023, 9, e14467. [Google Scholar] [CrossRef]
- Ashrafi, S.M. Two-stage metaheuristic mixed integer nonlinear programming approach to extract optimum hedging rules for multi-reservoir systems. J. Water Resour. Plan. Manag. 2021, 147, 04021070. [Google Scholar] [CrossRef]
- Yang, G.; Sun, W.; Xu, C. Optimizing rule curves for reservoir operation considering flood and drought situations. Water 2019, 11, 1639. [Google Scholar]
- Li, X.; Chen, Z.; Zhang, H. Adaptive rule curves for reservoir management in extreme situations. Water 2020, 12, 648. [Google Scholar]
- Kangrang, A.; Prasanchum, H. Active future rule curves for multi-purpose reservoir operation on the impact of climate and land use changes. J. Hydro-Environ. Res. 2019, 24, 1–13. [Google Scholar] [CrossRef]
- Thongwan, T.; Kangrang, A.; Prasanchum, H. Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation. Heliyon 2019, 5, e02401. [Google Scholar] [CrossRef] [PubMed]
- Ashrafi, S.M.; Dariane, A.B. Coupled operating rules for optimal operation of multi-reservoir systems. Water Resour. Manag. 2017, 31, 4505–4520. [Google Scholar] [CrossRef]
- Sriworamas, K.; Kangrang, A.; Thongwan, T.; Prasanchum, H. Optimal Reservoir of Small Reservoirs by Optimization Techniques on Reservoir Simulation Model 2021. Adv. Civil Eng. 2021, 2021, 6625743. [Google Scholar] [CrossRef]
- Chaleeraktrakoon, C.; Kangrang, A. Dynamic programming with the principle of progressive optimality for searching rule curves. Can. J. Civ. Eng. 2007, 34, 170–176. [Google Scholar] [CrossRef]
- Hu, Z.; Wang, W.; Zhang, H. Dynamic programming for identifying optimal rule curves in water resource management. Water 2017, 9, 460. [Google Scholar]
- Kangrang, A.; Compliew, S.; Chaiyapoom, W. Heuristic algorithm with simulation model for searching optimal reservoir rule curves. Am. J. Appl. Sci. 2009, 6, 263–267. [Google Scholar] [CrossRef]
- Zhang, Y.; Hu, C. Heuristic algorithms for optimizing rule curves in reservoir simulation models. Water 2021, 13, 823. [Google Scholar]
- Teegavarapu, R.; Simonovic, S. Optimal Operation of Reservoir Systems using Simulated Annealing. Water Resour. Manag. 2002, 16, 135–151. [Google Scholar] [CrossRef]
- Ashrafi, S.M.; Ashrafi, S.F.; Moazami, S. Developing self-adaptive melody search algorithm for optimal operation of multi-reservoir systems. J. Hydraul. Struct. 2017, 3, 35–48. [Google Scholar]
- Kangrang, A.; Compliew, S.; Hormwichian, R. Optimal reservoir rule curves using simulated annealing. In Proceedings of the Institution of Civil Engineers-Water Management; Thomas Telford Ltd.: London, UK, 2011; Volume 164, pp. 27–34. [Google Scholar]
- Li, X.; Wang, Q.; Li, S. Simulated annealing algorithm for searching optimal rule curves in reservoir operation. Water 2019, 11, 2034. [Google Scholar] [CrossRef] [Green Version]
- Kangrang, A.; Hormwichian, R. Optimal reservoir rule curves using conditional shuffled frog leaping algorithm and simulation. Int. J. Earth Sci. Eng. 2013, 6, 1392–1399. [Google Scholar]
- Zhou, L.; Chen, X.; Liu, J. Shuffled frog leaping algorithm for optimizing rule curves in reservoir management. Water 2020, 12, 3231. [Google Scholar]
- Wu, H.; Liu, Y.; Li, M. Genetic algorithm-based optimization of rule curves in reservoir simulation models. Water 2018, 10, 620. [Google Scholar]
- Prasanchum, H.; Kangrang, A. Optimal reservoir rule curves under climatic and land use changes for Lampao Dam using Genetic Algorithm. KSCE J. Civ. Eng. 2018, 22, 351–364. [Google Scholar] [CrossRef]
- Hormwichian, R.; Anongrit, K.; Alongkorn, L.; Chavalit, C.; Sanguan, P. Coupled-operations model and a conditional differential evolution algorithm for improving reservoir management. Int. J. Phys. Sci. 2012, 7, 5701–5710. [Google Scholar]
- Li, X.; Zhang, Q.; Chen, Z. Differential evolution algorithm for finding optimal rule curves in reservoir operation. Water 2021, 13, 1219. [Google Scholar]
- Zhou, X.; Wang, X.; Zhu, Z. The Model of Optimizing the Function of Reservoir Operation Based on Genetic Programming. In Proceedings of the International Conference on Machine Learning and Cybernetics, Beijing, China, 26–29 August 2002; Volume 1, pp. 286–290. [Google Scholar] [CrossRef]
- Chen, Z.; Li, J.; Zhang, H. Genetic programming approach to optimizing rule curves in reservoir simulation models. Water 2020, 12, 2757. [Google Scholar]
- Zhang, Y.; Chen, X.; Hu, C. Cultural algorithm for searching optimal rule curves in reservoir management. Water 2019, 11, 862. [Google Scholar]
- Phumiphan, A.; Kangrang, A. Development of decision-making support tools for future reservoir management under climate and land cover variability: A case study. Int. Rev. Civ. Eng. 2021, 12, 271. [Google Scholar] [CrossRef]
- Avesani, D.; Zanfei, A.; Di Marco, N.; Galletti, A.; Ravazzolo, F.; Righetti, M.; Majone, B. Short-term hydropower optimization driven by innovative time-adapting econometric model. Appl. Energy 2022, 310, 118510. [Google Scholar] [CrossRef]
- Kangrang, A.; Pakoktom, W.; Nuannukul, W.; Chaleeraktrakoon, C. Adaptive reservoir rule curves by optimisation and simulation. In Proceedings of the Institution of Civil Engineers-Water Management; Thomas Telford Ltd.: London, UK, 2017; Volume 170, pp. 219–230. [Google Scholar]
- Xu, L.; Li, X.; Liu, J. Cuckoo search algorithm for finding optimal rule curves in reservoir operation. Water 2021, 13, 1563. [Google Scholar]
- Zhao, W.; Wang, S.; Zhang, H. Firefly algorithm-based optimization of rule curves in reservoir management. Water 2020, 12, 1617. [Google Scholar]
- Liu, H.; Zhou, J.; Zhu, Y. Flower pollination algorithm for finding optimal rule curves in reservoir simulation models. Water 2018, 10, 1124. [Google Scholar]
- Wang, Q.; Chen, Z.; Li, X. Gray wolf optimizer for optimizing rule curves in reservoir operation. Water 2021, 13, 1083. [Google Scholar] [CrossRef]
- Wu, Y.; Li, L.; Huang, D. Wind-driven optimization algorithm and its application in model-free control of reservoir operation. Water Resour. Manag. 2015, 29, 4641–4656. [Google Scholar]
- Ghorbani, M.A.; Karamouz, M.; Zahmatkesh, Z. Reservoir operation optimization using wind driven optimization algorithm. J. Water Resour. Prot. 2016, 8, 1105–1121. [Google Scholar]
- Madani, K.; Izady, A. Optimal reservoir operation using ant colony optimization. Water Resour. Manag. 2011, 25, 2029–2046. [Google Scholar]
- Kangrang, A.; Lokham, C. Optimal reservoir rule curves considering conditional ant colony optimization with simulation model. J. Appl. Sci. 2013, 13, 154–160. [Google Scholar] [CrossRef]
- Kisi, O.; Ay, M. Reservoir operation optimization using ant colony optimization and harmony search algorithms. Water Resour. Manag. 2014, 28, 4487–4504. [Google Scholar]
- Tawfik, M.M.; Alazba, A.A.; Alghamdi, A.S.; El-Shafie, A. Honey-bee mating optimization algorithm for multi-reservoir system operation optimization. Water Resour. Manag. 2019, 33, 4159–4179. [Google Scholar]
- Asghari-Moghaddam, A.; Behmanesh, J.; Zolfaghari, S.; Maknoon, R. Reservoir operation optimization using honey-bee mating optimization algorithm. J. Water Resour. Plan. Manag. 2017, 143, 04017009. [Google Scholar]
- Songsaengrit, S.; Kangrang, A. Dynamic rule curves and streamflow under climate change for multipurpose reservoir operation using honey-bee mating optimization. Sustainability 2022, 14, 8599. [Google Scholar] [CrossRef]
- Techarungruengsakul, R.; Ngamsert, R.; Thongwan, T.; Hormwichian, R.; Kuntiyawichai, K.; Ashrafi, S.M.; Kangrang, A. Optimal choices in decision supporting system for network reservoir operation. Water 2022, 14, 4090. [Google Scholar] [CrossRef]
- Kangrang, A.; Chaleeraktrakoon, C. Suitable conditions of reservoir simulation for searching rule curves. J. Appl. Sci. 2008, 8, 1274–1279. [Google Scholar] [CrossRef] [Green Version]
- Zhao, T.; Zhao, J. Optimizing operation of water supply reservoir: The role of constraints. Math. Probl. Eng. 2014, 2014, 853186. [Google Scholar] [CrossRef] [Green Version]
- Hadiyan, P.; Moeini, R.; Ehsanzadeh, E. Application of static and dynamic artificial neural networks for forecasting inflow discharges, case study: Sefidroud Dam reservoir. Sustain. Comput. Inform. Syst. 2020, 27, 100401. [Google Scholar] [CrossRef]
- Sullis, A. An optimisation model for reservoir operation. In Proceedings of the Institution of Civil Engineers-Water Management; Thomas Telford Ltd.: London, UK, 2017; Volume 170, pp. 175–183. [Google Scholar]
- Ashrafi, S.M.; Mostaghimzadeh, E.; Adib, A. Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves. Hydrol. Sci. J. 2020, 65, 2007–2021. [Google Scholar] [CrossRef]
- Chang, F.J.; Chen, L.; Chang, L.C. Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol. Process. 2005, 19, 2277–2289. [Google Scholar] [CrossRef]
- Techarungruengsakul, R.; Kangrang, A. Application of harris hawks optimization with reservoir simulation model considering hedging rule for network reservoir system. Sustainability 2022, 14, 4913. [Google Scholar] [CrossRef]
- Ficklin, D.; Zhang, M. A Comparison of the Curve Number and Green-Ampt Models in an Agricultural Watershed. Trans. ASABE 2013, 56, 61–69. [Google Scholar] [CrossRef]
- Shuster, W.; Pappas, E. Laboratory Simulation of Urban Runoff and Estimation of Runoff Hydrographs with Experimental Curve Numbers Implemented in USEPA SWMM. J. Irrig. Drain. Eng. 2011, 137, 343–351. [Google Scholar] [CrossRef]
- Akbari-Alashti, H.; Bozorg-Haddad, O.; Fallah-Mehdipour, E.; Mariño, M.A. Multi-reservoir real-time operation rules: A new genetic programming approach. In Proceedings of the Institution of Civil Engineers-Water Management; Thomas Telford Ltd.: London, UK, 2014; Volume 167, pp. 561–576. [Google Scholar]
- Yang, S.; Yang, D.; Chen, J.; Zhao, B. Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model. J. Hydrol. 2019, 579, 124229. [Google Scholar] [CrossRef]
- Beshavard, M.; Adib, A.; Ashrafi, S.M.; Kisi, O. Establishing effective warning storage to derive optimal reservoir operation policy based on the drought condition. Agric. Water Manag. 2022, 274, 107948. [Google Scholar] [CrossRef]
- Maddu, R.; Pradhan, I.; Ahmadisharaf, E.; Singh, S.K.; Shaik, R. Short-range reservoir inflow forecasting using hydrological and large-scale atmospheric circulation information. J. Hydrol. 2022, 612, 128153. [Google Scholar] [CrossRef]
- Mostaghimzadeh, E.; Ashrafi, S.M.; Adib, A.; Geem, Z.W. Investigation of Forecast Accuracy and its Impact on the Efficiency of Data-Driven Forecast-Based Reservoir Operating Rules. Water 2021, 13, 2737. [Google Scholar] [CrossRef]
- Mostaghimzadeh, E.; Adib, A.; Ashrafi, S.M.; Kisi, O. Investigation of a composite two-phase hedging rule policy for a multi reservoir system using streamflow forecast. Agric. Water Manag. 2022, 265, 107542. [Google Scholar] [CrossRef]
- Zhao, Q.; Cai, X.; Li, Y. Determining inflow forecast horizon for reservoir operation. Water Resour. Res. 2019, 55, 4066–4081. [Google Scholar] [CrossRef]
- Zarei, M.; Bozorg-Haddad, O.; Baghban, S.; Delpasand, M.; Goharian, E.; Loáiciga, H.A. Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages. Sci. Rep. 2021, 11, 24295. [Google Scholar] [CrossRef]
- Mostaghimzadeh, E.; Ashrafi, S.M.; Adib, A.; Geem, Z.W. A long lead time forecast model applying an ensemble approach for managing the great Karun multi-reservoir system. Appl. Water Sci. 2023, 13, 124. [Google Scholar] [CrossRef]
- Bakhsipoor, I.E.; Ashrafi, S.M.; Adib, A. Water quality effects on the optimal water resources operation in Great Karun River Basin. Pertanika J. Sci. Technol. 2019, 27, 1881–1900. [Google Scholar]
- Zhou, J.; Jia, B.; Chen, X.; Qin, H.; He, Z.; Liu, G. Identifying Efficient Operating Rules for Hydropower Reservoirs Using System Dynamics Approach—A Case Study of Three Gorges Reservoir, China. Water 2019, 11, 2448. [Google Scholar] [CrossRef] [Green Version]
- Wang, K.; Shi, H.; Chen, J.; Li, T. An improved operation-based reservoir scheme integrated with Variable Infiltration Capacity model for multiyear and multipurpose reservoirs. J. Hydrol. 2019, 571, 365–375. [Google Scholar] [CrossRef]
- Ashrafi, S.M. Investigating pareto front extreme policies using semi-distributed simulation model for Great Karun River Basin. J. Hydraul. Struct. 2019, 5, 75–88. [Google Scholar]
- Li, J.; Huang, J.; Liang, P.; Lund, J. Fuzzy Representation of Environmental Flow in Multi-Objective Risk Analysis of Reservoir Operation. Water Resour. Manag. 2021, 35, 2845–2861. [Google Scholar] [CrossRef]
- Afshar, A.; Masoumi, F. Waste load reallocation in river–reservoir systems: Simulation–optimization approach. Environ. Earth Sci. 2015, 75, 53. [Google Scholar] [CrossRef]
- United States Army Corps of Engineers (USACE). Hydrologic Engineering Methods for Water Resources Development; Reservoir Yield; US Army Corps of Engineers: Davis, CA, USA, 1975; Volume 8. [Google Scholar]
- Kangrang, A.; Chaleeraktrakoon, C. Genetic algorithms connected simulation with smoothing function for searching rule curves. Am. J. Appl. Sci. 2007, 4, 73–79. [Google Scholar] [CrossRef]
- Chaleeraktrakoon, C. Stochastic procedure for generating seasonal flows. J. Hydrol. Eng. 1999, 4, 337–343. [Google Scholar] [CrossRef]
- Kangrang, A.; Hormwichian, R.; Pramual, P.; Wongpakam, K. An improvement of reservoir rule curves for increasing storage capacity. ARPN J. Eng. Appl. Sci. 2019, 14, 1340–1356. [Google Scholar]
- Yin, X.; Yang, Z.; Petts, G.; Kondolf, G. A reservoir operating method for riverine ecosystem protection, reservoir sedimentation control and water supply. J. Hydrol. 2014, 512, 379–387. [Google Scholar] [CrossRef]
- Azlan, N.N.I.; Saad, N.; Norhisham, S.; Abdul Malek, M.; Shkuri, N.; Zolkepli, M.; Lee Woen, E.; Mohamad, A.M. Water Demand Management at Rural Area Using Micro-Component Analysis: A Case Study at Kenyir Lake, Malaysia. IOP Conf. Ser. Earth Environ. Sci. 2022, 955, 012027. [Google Scholar] [CrossRef]
- Lee, J.; Shin, H. Agricultural Reservoir Operation Strategy Considering Climate and Policy Changes. Sustainability 2022, 14, 9014. [Google Scholar] [CrossRef]
- Hormwichian, R.; Kangrang, A.; Lamom, A. A conditional genetic algorithm model for searching optimal reservoir rule curves. J. Appl. Sci. 2009, 9, 3375–3380. [Google Scholar]
- Kangrang, A.; Chaleeraktrakoon, C.; Patamatamkul, S.; Hormwichian, R. Expert Participation with Optimization Technique for Improving Optimal Rule Curves of Reservoir. Bulg. J. Agric. Sci. 2013, 19, 1146–1153. [Google Scholar]
- Leta, M.; Demissie, T.; Tränckner, J. Optimal Operation of Nashe Hydropower Reservoir under Land Use Land Cover Change in Blue Nile River Basin. Water 2022, 14, 1606. [Google Scholar] [CrossRef]
- Fallah-Mehdipour, E.; Haddad, O.; Mariño, M. Developing Reservoir Operational Decision Rule by Genetic Programming. J. Hydroinformatics 2013, 15, 1293–1310. [Google Scholar] [CrossRef]
- Tang, B.; Geng, C.; Huang, M.; Lu, H.; Ren, K. Research on the Depletion and Recovery Characteristics of Fault-Karst Reservoirs. Geofluids 2022, 2022, 1105335. [Google Scholar] [CrossRef]
- Ding, Y.; Tang, D.; Meng, Z. A New Functional Approach for Searching Optimal Reservoir Rule Curves. Adv. Mater. Res. 2014, 915–916, 1452–1455. [Google Scholar] [CrossRef]
- Kayhomayoon, Z.; Milan, S.; Azar, N.; Bettinger, P.; Babaian, F.; Jaafari, A. A Simulation-Optimization Modeling Approach for Conjunctive Water Use Management in a Semi-Arid Region of Iran. Sustainability 2022, 14, 2691. [Google Scholar] [CrossRef]
- Tareke, K.; Awoke, A. Hydrological Drought Analysis using Streamflow Drought Index (SDI) in Ethiopia. Adv. Meteorol. 2022, 2022, 7067951. [Google Scholar] [CrossRef]
- Jiang, H.; Simonovic, S.; Yu, Z.; Wang, W. A System Dynamics Simulation Approach for Environmentally Friendly Operation of a Reservoir System. J. Hydrol. 2020, 591, 124971. [Google Scholar] [CrossRef]
- Merdeka, M.; Ridha, S.; Negash, B.; Ilyas, S. Reservoir Performance Prediction in Steam Huff and Puff Injection Using Proxy Modeling. Appl. Sci. 2022, 12, 3169. [Google Scholar] [CrossRef]
- Keith, C.; Wang, X.; Zhang, Y.; Dandekar, A.; Ning, S.; Wang, D. Oil Recovery Prediction for Polymer Flood Field Test of Heavy Oil on Alaska North Slope via Machine Assisted Reservoir Simulation. In Proceedings of the SPE Improved Oil Recovery Conference, Online, 25–29 April 2022. [Google Scholar]
- Tan, Y.; Dong, Z.; Xiong, C.; Zhong, Z.; Hou, L. An Optimal Allocation Model for Large Complex Water Resources System Considering Water Supply and Ecological Needs. Water 2019, 11, 843. [Google Scholar] [CrossRef] [Green Version]
- Hong, J. Parameter Optimization of Agricultural Reservoir Long-Term Runoff Model Based on Historical Data. J. Korea Water Resour. Assoc. 2021, 54, 733–743. [Google Scholar]
- Malallah, A.; Al-Ashwak, A.; Nashawi, I. Infill Well Placement Optimization in Two-Dimensional Heterogeneous Reservoirs under Waterflooding Using Upscaling Wavelet Transform. J. Pet. Sci. Eng. 2021, 202, 109344. [Google Scholar] [CrossRef]
- Al-Aqeeli, Y.; Altaiee, T.; Abdulmawjood, A. Proposition of a Multi-Reservoir System Across the Border of Riparian Countries and Specifying Its Operational Outputs by Formulating Simulation Models. Water Resour. Manag. 2021, 35, 3355–3372. [Google Scholar] [CrossRef]
- Eriyagama, N.; Smakhtin, V.; Udamulla, L. Sustainable Surface Water Storage Development: Measuring Economic Benefits and Ecological and Social Impacts of Reservoir System Configurations. Water 2022, 14, 307. [Google Scholar] [CrossRef]
- Kangrang, A.; Prasanchum, H.; Hormwichian, R.; Techarungruengsakul, R.; Ngamsert, R.; Phookinghin, N.; Wangthken, J. Improvement of water management project by correcting irrigation water requirement in farmer participation and optimization. Bulg. J. Agric. Sci. 2019, 25, 852–863. [Google Scholar]
- Thongwan, T.; Kangrang, A.; Techarungruengsakul, R.; Ngamsert, R. Future inflow under land use and climate changes and participation process into the medium-sized reservoirs in Thailand. Adv. Civ. Eng. 2020, 2020, 5812530. [Google Scholar] [CrossRef]
- Kim, Y.; Sun, B.; Kim, P.; Jo, M.; Ri, T.; Pak, G. A Study on Optimal Operation of Gate-Controlled Reservoir System for Flood Control Based on PSO Algorithm Combined with Rearrangement Method of Partial Solution Groups. J. Hydrol. 2021, 593, 125783. [Google Scholar] [CrossRef]
- Niu, W.; Feng, Z. Evaluating the Performances of Several Artificial Intelligence Methods in Forecasting Daily Streamflow Time Series for Sustainable Water Resources Management. Sustain. Cities Soc. 2021, 74, 103176. [Google Scholar] [CrossRef]
Duration Lengths of Inflow Records (Years) | Frequency | Magnitude (MCM/Year) | Duration (Year) | |||
---|---|---|---|---|---|---|
(Times/Year) | Average | Maximum | Average | Maximum | ||
4 | μ | 0.157 | 34 | 495 | 2.2 | 3.4 |
σ | 0.084 | 28 | 379 | 1.0 | 1.7 | |
7 | μ | 0.191 | 35 | 431 | 2.1 | 3.6 |
σ | 0.089 | 23 | 257 | 0.9 | 1.8 | |
10 | μ | 0.118 | 22 | 313 | 2.0 | 2.8 |
σ | 0.072 | 18 | 210 | 1.0 | 1.6 | |
14 | μ | 0.124 | 23 | 329 | 2.0 | 2.9 |
σ | 0.072 | 18 | 205 | 0.9 | 1.6 | |
17 | μ | 0.137 | 24 | 323 | 2.0 | 3.1 |
σ | 0.076 | 18 | 206 | 1.0 | 1.7 | |
21 | μ | 0.137 | 24 | 323 | 2.0 | 3.1 |
σ | 0.076 | 18 | 206 | 1.0 | 1.7 | |
26 | μ | 0.137 | 24 | 323 | 2.0 | 3.1 |
σ | 0.076 | 18 | 206 | 1.0 | 1.7 | |
45 | μ | 0.113 | 19 | 289 | 1.9 | 2.7 |
σ | 0.070 | 16 | 219 | 0.9 | 1.6 |
Duration Lengths of Inflow Records (Years) | Frequency | Magnitude (MCM/Year) | Duration (Year) | |||
---|---|---|---|---|---|---|
(Times/Year) | Average | Maximum | Average | Maximum | ||
4 | μ | 0.833 | 1179 | 4537 | 7.9 | 16.5 |
σ | 0.073 | 196 | 1842 | 4.2 | 6.6 | |
7 | μ | 0.819 | 1172 | 4524 | 7.0 | 15.1 |
σ | 0.074 | 197 | 1831 | 3.1 | 5.7 | |
10 | μ | 0.815 | 1121 | 4437 | 7.0 | 15.4 |
σ | 0.079 | 196 | 1850 | 3.0 | 5.9 | |
14 | μ | 0.819 | 1124 | 4537 | 7.3 | 15.6 |
σ | 0.078 | 197 | 1892 | 3.4 | 6.1 | |
17 | μ | 0.816 | 1108 | 4545 | 7.2 | 15.3 |
σ | 0.079 | 198 | 1862 | 3.3 | 5.9 | |
21 | μ | 0.816 | 1108 | 4545 | 7.2 | 15.3 |
σ | 0.079 | 198 | 1862 | 3.3 | 5.9 | |
26 | μ | 0.816 | 1108 | 4545 | 7.2 | 15.3 |
σ | 0.079 | 198 | 1862 | 3.3 | 5.9 | |
45 | μ | 0.811 | 1110 | 4706 | 7.0 | 15.0 |
σ | 0.079 | 202 | 1905 | 3.1 | 5.6 |
Objective Functions | Frequency | Magnitude (MCM/Year) | Duration (Year) | |||
---|---|---|---|---|---|---|
(Times/Year) | Average | Maximum | Average | Maximum | ||
SI | μ | 0.142 | 25 | 391 | 2.0 | 3.1 |
σ | 0.080 | 21 | 243 | 0.9 | 1.7 | |
Aver | μ | 0.208 | 29 | 207 | 2.1 | 3.8 |
σ | 0.082 | 14 | 102 | 0.7 | 1.8 | |
Max | μ | 0.807 | 139 | 437 | 8.5 | 18.1 |
σ | 0.092 | 30 | 98 | 5.4 | 7.6 | |
Fre | μ | 0.123 | 45 | 707 | 1.9 | 2.8 |
σ | 0.070 | 35 | 457 | 0.8 | 1.5 | |
RMS | μ | 0.196 | 31 | 333 | 2.1 | 3.7 |
σ | 0.087 | 19 | 177 | 0.8 | 1.7 | |
SUM | μ | 0.156 | 28 | 353 | 2.0 | 3.3 |
σ | 0.078 | 19 | 218 | 0.8 | 1.7 |
Objective Functions | Frequency | Magnitude (MCM/Year) | Duration (Year) | |||
---|---|---|---|---|---|---|
(Times/Year) | Average | Maximum | Average | Maximum | ||
SI | μ | 0.848 | 1038 | 4757 | 9.0 | 18.2 |
σ | 0.072 | 205 | 2002 | 5.2 | 7.1 | |
Aver | μ | 0.843 | 1188 | 4446 | 8.8 | 17.2 |
σ | 0.073 | 194 | 1825 | 5.6 | 7.0 | |
Max | μ | 0.856 | 1160 | 5403 | 8.3 | 17.1 |
σ | 0.065 | 208 | 2031 | 4.2 | 6.5 | |
Fre | μ | 0.814 | 1036 | 4882 | 7.1 | 15.4 |
σ | 0.078 | 205 | 1932 | 3.2 | 5.9 | |
RMS | μ | 0.836 | 1289 | 5578 | 7.9 | 16.3 |
σ | 0.071 | 231 | 2014 | 4.2 | 6.3 | |
SUM | μ | 0.848 | 1082 | 4602 | 8.7 | 17.7 |
σ | 0.071 | 196 | 1922 | 4.4 | 6.7 |
Situations | Rule Curves | Frequency (Times/Year) | Volume (MCM.) | Time Period (Year) | ||
---|---|---|---|---|---|---|
Average | Maximum | Average | Maximum | |||
Shortage | Existing | 0.792 | 4.917 | 12.000 | 3.8000 | 8.000 |
GP Agripar-D | 0.501 | 2.215 | 9.000 | 2.400 | 4.000 | |
GP Agripar-Dpar | 0.633 | 3.135 | 10.000 | 5.000 | 6.000 | |
Excess water | Existing | 0.958 | 9.190 | 15.879 | 11.500 | 19.000 |
GP Agripar-D | 0.874 | 6.432 | 13.709 | 5.250 | 12.000 | |
GP Agripar-Dpar | 1.000 | 9.395 | 13.800 | 5.500 | 12.000 |
Situations | Rule Curves | Frequency (Times/Year) | Volume (MCM.) | Time Period (Year) | ||
---|---|---|---|---|---|---|
Average | Maximum | Average | Maximum | |||
Shortage | Existing | 0.474 | 0.789 | 2.000 | 2.250 | 3.000 |
GP Agripar-D | 0.346 | 0.643 | 2.400 | 2.035 | 4.000 | |
GP Agripar-Dpar | 0.323 | 0.639 | 2.100 | 1.500 | 3.000 | |
Excess water | Existing | 1.000 | 10.303 | 20.834 | 18.000 | 19.000 |
GP Agripar-D | 1.000 | 9.963 | 20.832 | 18.000 | 19.000 | |
GP Agripar-Dpar | 1.000 | 9.946 | 20.784 | 18.000 | 19.000 |
Initial Capacity of Reservoir (%) | Frequency | Magnitude (MCM/Year) | Duration (Year) | |||
---|---|---|---|---|---|---|
(Times/Year) | Average | Maximum | Average | Maximum | ||
10 | μ | 0.113 | 20 | 300 | 1.9 | 2.7 |
σ | 0.071 | 17 | 207 | 0.9 | 1.6 | |
20 | μ | 0.114 | 20 | 306 | 1.9 | 2.7 |
σ | 0.072 | 18 | 204 | 0.9 | 1.8 | |
30 | μ | 0.114 | 21 | 314 | 1.9 | 2.8 |
σ | 0.073 | 18 | 220 | 0.9 | 1.6 | |
40 | μ | 0.118 | 21 | 316 | 1.9 | 2.8 |
σ | 0.072 | 18 | 227 | 0.9 | 1.6 | |
50 | μ | 0.136 | 23 | 326 | 2.0 | 3.1 |
σ | 0.077 | 18 | 220 | 1.0 | 1.7 | |
60 | μ | 0.126 | 22 | 313 | 1.9 | 2.9 |
σ | 0.072 | 18 | 206 | 0.8 | 1.5 | |
70 | μ | 0.102 | 19 | 309 | 1.8 | 2.5 |
σ | 0.067 | 17 | 220 | 1.0 | 1.5 | |
80 | μ | 0.137 | 24 | 323 | 2.0 | 3.1 |
σ | 0.076 | 18 | 206 | 1.0 | 1.7 | |
90 | μ | 0.137 | 24 | 323 | 2.0 | 3.1 |
σ | 0.076 | 18 | 206 | 1.0 | 1.7 | |
100 | μ | 0.137 | 24 | 323 | 2.0 | 3.1 |
σ | 0.076 | 18 | 206 | 1.0 | 1.7 |
Initial Capacity of Reservoir (%) | Frequency | Magnitude (MCM/Year) | Duration (Year) | |||
---|---|---|---|---|---|---|
(Times/Year) | Average | Maximum | Average | Maximum | ||
10 | μ | 0.812 | 1113 | 4579 | 7.0 | 15.1 |
σ | 0.080 | 195 | 1918 | 3.2 | 5.6 | |
20 | μ | 0.812 | 1117 | 4574 | 7.0 | 15.1 |
σ | 0.079 | 197 | 1908 | 3.2 | 5.7 | |
30 | μ | 0.812 | 1124 | 4637 | 7.0 | 15.1 |
σ | 0.079 | 203 | 1893 | 3.3 | 5.9 | |
40 | μ | 0.816 | 1132 | 4532 | 7.2 | 15.4 |
σ | 0.079 | 202 | 1836 | 3.2 | 5.7 | |
50 | μ | 0.819 | 1114 | 4497 | 7.4 | 15.7 |
σ | 0.080 | 196 | 1883 | 3.5 | 6.1 | |
60 | μ | 0.815 | 1102 | 4477 | 7.1 | 15.2 |
σ | 0.079 | 195 | 1879 | 3.3 | 5.8 | |
70 | μ | 0.817 | 1121 | 4558 | 7.4 | 15.7 |
σ | 0.080 | 205 | 1828 | 3.5 | 6.1 | |
80 | μ | 0.816 | 1108 | 4545 | 7.2 | 15.3 |
σ | 0.079 | 198 | 1862 | 3.3 | 5.9 | |
90 | μ | 0.816 | 1108 | 4545 | 7.2 | 15.3 |
σ | 0.079 | 198 | 1862 | 3.3 | 5.9 | |
100 | μ | 0.816 | 1108 | 4545 | 7.2 | 15.3 |
σ | 0.079 | 198 | 1862 | 3.3 | 5.9 |
Situation | Rule Curve | Frequency (Times/Year) | Magnitude (MCM/Year) | Duration (Years) | |||
---|---|---|---|---|---|---|---|
Average | Maximum | Average | Maximum | ||||
Water shortage | RC1 | μ | 0.180 | 21.024 | 340.800 | 2.2 | 3.3 |
σ | 0.052 | 9.149 | 116.722 | 0.6 | 1.1 | ||
RC2-A2 | μ | 0.260 | 32.475 | 398.000 | 2.4 | 3.9 | |
σ | 0.062 | 10.138 | 115.097 | 0.6 | 1.3 | ||
RC2-B2 | μ | 0.284 | 33.440 | 394.770 | 2.4 | 4.3 | |
σ | 0.066 | 10.024 | 121.184 | 0.6 | 1.5 | ||
RC3 | μ | 0.512 | 113.104 | 597.560 | 2.8 | 6.4 | |
σ | 0.052 | 13.484 | 93.274 | 0.5 | 2.0 | ||
Excess release | RC1 | μ | 0.878 | 990.632 | 4204.398 | 9.3 | 19.3 |
σ | 0.041 | 17.546 | 781.166 | 3.3 | 6.7 | ||
RC2-A2 | μ | 0.881 | 1004.786 | 4196.273 | 8.9 | 19.5 | |
σ | 0.036 | 18.213 | 804.820 | 2.8 | 7.0 | ||
RC2-B2 | μ | 0.875 | 1001.375 | 4201.483 | 8.6 | 18.7 | |
σ | 0.038 | 18.565 | 832.729 | 2.9 | 6.5 | ||
RC3 | μ | 0.957 | 1098.163 | 4337.941 | 20.7 | 30.9 | |
σ | 0.026 | 21.655 | 828.049 | 13.1 | 11.1 |
Situation | Rule Curve | Frequency (Times/Year) | Magnitude (MCM/Year) | Duration (Years) | ||
---|---|---|---|---|---|---|
Average | Maximum | Average | Maximum | |||
Water shortage | RC1 | 0.320 | 33.740 | 223.000 | 1.6 | 3.0 |
RC2-A2 | 0.200 | 5.780 | 154.000 | 1.3 | 2.0 | |
RC2-B2 | 0.060 | 5.980 | 167.000 | 1.5 | 2.0 | |
RC3 | 0.260 | 37.980 | 475.000 | 1.3 | 2.0 | |
Excess release | RC1 | 0.940 | 1533.717 | 7000.341 | 11.8 | 33.0 |
RC2-A2 | 0.940 | 1508.881 | 7000.341 | 11.8 | 26.0 | |
RC2-B2 | 0.940 | 1504.936 | 7026.783 | 11.8 | 26.0 | |
RC3 | 0.940 | 1558.373 | 7000.341 | 11.8 | 26.0 |
Situation | Rule Curve | Frequency (Times/Year) | Magnitude (MCM/Year) | Duration (Years) | ||
---|---|---|---|---|---|---|
Average | Maximum | Average | Maximum | |||
Water shortage | RC1 | 0.300 | 38.860 | 277.000 | 1.3 | 2.0 |
RC2-A2 | 0.060 | 9.840 | 408.000 | 1.5 | 2.0 | |
RC2-B2 | 0.060 | 9.240 | 352.000 | 1.5 | 2.0 | |
RC3 | 0.140 | 23.200 | 660.000 | 1.2 | 2.0 | |
Excess release | RC1 | 0.980 | 2084.397 | 4699.003 | 24.5 | 27.0 |
RC2-A2 | 0.980 | 2054.251 | 4681.015 | 24.5 | 27.0 | |
RC2-B2 | 1.000 | 2048.272 | 4690.969 | 50.0 | 50.0 | |
RC3 | 0.980 | 2085.463 | 4711.864 | 24.5 | 27.0 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sriworamas, K.; Prasanchum, H.; Ashrafi, S.M.; Hormwichian, R.; Techarungruengsakul, R.; Ngamsert, R.; Chaiyason, T.; Kangrang, A. Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves. Water 2023, 15, 2501. https://doi.org/10.3390/w15132501
Sriworamas K, Prasanchum H, Ashrafi SM, Hormwichian R, Techarungruengsakul R, Ngamsert R, Chaiyason T, Kangrang A. Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves. Water. 2023; 15(13):2501. https://doi.org/10.3390/w15132501
Chicago/Turabian StyleSriworamas, Krit, Haris Prasanchum, Seyed Mohammad Ashrafi, Rattana Hormwichian, Rapeepat Techarungruengsakul, Ratsuda Ngamsert, Teerajet Chaiyason, and Anongrit Kangrang. 2023. "Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves" Water 15, no. 13: 2501. https://doi.org/10.3390/w15132501