Management of water demand by use a new dynamic firefly algorithm: case study, River Hirmand

Subject Areas : Subjects

Zahra Ghaffari Moghadam ¹ (Faculty member, Institute of Agriculture, University of Zabol and Ph.D. student of Agricultural Economic University of Sistan and Balochestan, Iran.)
mahmoud hashemitabar ² (Assistant professor, Agricultural Economics Department, University of Sistan and Balochestan, Zahedan, Iran.)
ebrahim moradi ³ (Assistant professor, Agricultural Economics Department, University of Sistan and Balochestan, Zahedan, Iran.)

Received: 2020-02-04 Accepted : 2020-11-02 Published : 2020-10-22

Keywords: Swarm intelligence, Dynamic parameter, Firefly algorithm, Optumization, predict,

Abstract :

Firefly algorithm (FA) is an effective optimization technique based on swarm intelligence, which has been successfully applied to various practical engineering problems. In this paper a new dynamic firefly algorithm is applied for demand estimation of water of river Hirmand in Sistan area and comparative with different firefly algorithm for 2006-2017 years. The data from 2006 to 2014 are used for learning and teaching and finding the optimal weights of the model, and the rest of data (2015-2017) are applied to test the models. The results show that all five FA variants can achieve promising solutions. But NDFA obtains better performance than four other FA variants and its prediction accuracy is up to 97.98%. After ensuring the accuracy of the algorithm, water demand of river Hirmand from 2018-2020 is predicted. And risk assessment of the water shortage under three scenarios of high, average, and low inflow levels for this years.

References:

1)       سردار شهرکی ع، 1395. تخصیص بهینه منابع آب حوزه هیرمند با کاربرد تئوری بازی و ارزیابی سناریوهای مدیریتی. پایان نامه دکتری. دانشگاه سیستان و بلوچستان

2)       سازمان حفاظت محیط زیست، مطالعه احیاء بخش هایی از تالاب بین المللی هامون (گودی های طیعی)، معاونت محیط زیست طبیعی، پژوهشکده تالاب بین المللی هامون، 1394.

3)       وزارت نیرو، گزارش برنامه ریزی منابع آب رودخانه و مخازن چاه نیمه‏های سیستان، جلد دوم، شرکت سهامی آب منطقه ای استان سیستان و یلوچستان، زابل، 1390.

4)       نوری ص، شهرکی ج،  سردار شهرکی ع. 1398. کاربرد الگوریتم فراابتکاری کرم شب تاب در تخصیص بهینه‏ی منابع آب چاه نیممه‏ی سیستان تحت سناریوی توسعه‏ی کشاورزی. مجله‏ی مهندسی منابع آب، 12(43): 63-74.

5)        Alah yari khamaneh P. and Taghavi afshrd S. 2017. Improve the accuracy of the firefly algorithm by bat algorithm. 5th international conference Electrical engineering and computer with emphasis on indigenous knowledge, Moghadas ardabili of higher education. (In Persian)

6)        Al-Zahrani M.A, and Abo-Monaar A. 2015. Urban residential water demand prediction based on artificial neural networks and time series models. Water Resource Management. 29(10): 3651-3662.

7)        Assareh E, Behrang MA, Assari MR, and Ghanbarzadeh A. 2010. Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in iran. Energy. 35: 5223-5229. (In Persian)

8)        Bai Y, Wang P, Chuan L, Jingjing X and Wang Y. 2014. A multi-scale relevance vector regression approach for daily urban water demand forecasting. Journal of Hydrology. 517:236-245.

9)        Bougadis J, Adamowski KB, and Diduch R. 2005. Short-term municipal water demand forecasting. Hydrological Processes. 19(1):137-148.

10)    Brentan BM, Luvizotto Jr, Herrera M, Izquierdo J, and Perez-Garcia R. 2017. Hybrid regression model for near real- time urban water demand forecasting. Journal Computer Applied Mathmatical. 309: 532-541.

11)    Chhikara RR, Sharma P, Singh L. 2018. An improved dynamic discrete firefly algorithm for blind image steganalysis. Int. J. Mach. Learn. & Cyber. 9: 821–835

12)    Cheng NJ, Ding XM, and Shen HB. 2014. Adaptive firefly algorithm: parameter analysis and its application. PIoS One. 9(11). E112634.

13)    Cui LZ, Li GH, Wang XZ, Lin QZ, Chen JY, Lu N, and Lu J. 2017. A ranking- based adaptive artificial bee colony algorithm for global numerical optimization. Inf Sci. 417: 169-185.

14)    Cui ZH, Sun B, Wang GG, Xue Y, and Chn JJ. 2017. A novel oriented cuckoo search algorithm to improve DV-hop performance for cyber-physical systems. Journal parallel Distrib. Comput. 103:42-52.

15)    Dorigo M, Maniezzo V, and Colorni A. 1996. Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B. 26: 29-41.

16)    Eslamian SA, Li SS, and Haghighat F. 2016. A new multiple regression model for predictions of urban water use. Sustainable Cities and Society. 27:419-429.

17)    Ehteram M, Karami H, Farzin S. 2018a. Reservoir optimization for energy production using a new evolutionary algorithm based on multi-criteria decision-making models. Water Resour Manag. 32(7):2539–2560

18)    Fister JR, Yang XS, Fister I, and Brest J. 2012. Memetic firefly algorithm for combinatorial optimization. In bioinspired optimization methods and their applications (BIOMA):1-14.

19)    Ghasemi vajani H. 2018. Expand an Artificial Neural Networks for forecasting karaj monthly drink water demand. Journal of Science and Engineering Elites. 3(5):67-82.(In Persian)

20)    Guo WQ, Shao DG, and Jiang YF. 2012. Risk evaluation of water shortage in source area of middle route project for south- to North water transfer in china. Water resource management. 26(12):3479-3493.   

21)    Hashimoto T, Stedinger JR, and Loucks DP. 1982. Reliability resiliency and vulnerability criteria for water resources system performance evaluation. Journal water resources research. 18(1):14-20.

22)    Hosseini-Moghari M, and Banihabib ME. 2014. Optimizing operation of reservoir for agricultural water supply using firefly algorithm. Journal of Water and soil resources conservation. 3(4):17-31.(In Persian)

23)    Kazemi M, Jalili ghazizadeh MR, and Darabi A. 2017. A review of some of the methods for forecasting urban water demand. 1th national conference on water loss & consumption management,Iran.

24)    Li M, Guo P, Singh V, and Yang G. 2016. An Uncertainty- based framework for agricultural water- land resources allocation and risk evaluation. Agricultural water management. 177:120-23.

25)    Maidment DR, Miaou SP, and Crawford MM. 1985. Transfer function models of daily urban water use. Journal of Water Resources Research. 21(4):425-432.

26)    Matsushita H. 2015. Firefly algorithm with dynamically changing connections.  IEEE Congress on Evolutionary Computation (CEC), Sendai. 2672-2677.

27)    Oliveira PJ, Steffan JL, and Cheung P. 2017. Parameter estimation of seasonal ARIMA models for water demand forecasting using the harmony search algorithm. Procedia Engineering. 186:177-185.

28)    Pulido-Calvo I, and Gutierrez-Estrada JC. 2009. Improved irrigation water demand forecasting using a soft-computing hybrid model. Bio systems engineering. 102(2):202-218.

29)    Ruan BQ, Han YP, Wang H, and Jiang RF. 2005. Fuzzy comprehensive assessment of water shortage risk. Journal Hydraul Eng. 36(8): 906-912. 

30)    Schleich J, and Hillenbrand T. 2009. Determinants of residential water demand in Germany. Ecological Economics. 68(6):756 -1769.

31)    Tabesh M, and Dini M. 2010. Forecasting Daily Urban Water Demand Using Artificial Neural Networks, A Case Study of Tehran Urban Water.  Journal of Water and Wastewater. 21(1): 84-95. (In Persian)

32)    Tabesh M, Dini M, Khoshkholgh AJ, and Zahraie B. 2008. Estimation of Tehran Daily Water Demand Using Time Series Analysis. Iran-water resources research. 4(2):57-65. (In Persian)

33)     Ufuk Sahin A. 2018. A Particle Swarm Optimization Assessment for the Determination of Non-Darcian Flow Parameters in a Confined Aquifer. Water Resources Management. 32(2):751-767.

34)    Wang H, Cui Z, Wang W, Zhou X, Zhao J, and Li Y. 2018. A new dynamic firefly algorithm for demand estimation of water resources. Information Sciences. 438: 95-106.

35)    Wang H, Zhihua C, Wenjun W, Xinyu Z, Zhao J, Lv L, and Sun H. 2017. Firefly aigorithm for demand estimation of water resources. Published in ICONIP. part IV, Lncs 10637: 11-20.

36)    Wang H, Zhou X, Sun H, Yu X, Zhao J, Zhang H, and Cui L. 2017. Firefly algorithm with adaptive control parameters. Soft Comput. 21(17):5091-5120.

37)    Wang H, Sun H, Li C, Rahnamayan S, and Pan GS. 2013. Diversity enhanced particle swarm optimization with neighborhood search. Information Sciences. 223: 119-135.

38)    Yang XS. 2008. Nature-inspired metaheuristic algorithms, Luniver Press, UK.

39)    Yang XS. 2010. Engineering Optimization an introduction with metaheuristic application. Gohn Wiely and Sons, USA.

40)    Yu SH, Zhu SL, Ma Y, and Mao DM. 2015. A variable step size firefly algorithm for numerical optimization. Appl Math Comput. 263: 214-220.

41)    Yin Z, Du C, Liu J. 2018. Research on Auto disturbance-Rejection Control of Induction Motors Based on an Ant Colony Optimization Algorithm. IEEE Transactions on Industrial Electronics, 65(4):3077-3094.

42)    Zarei A, Mousavi SF, Eshaghi Gordji M, Karami H.  2019.   Optimal Reservoir Operation Using Bat and Particle Swarm Algorithm and Game Theory Based on Optimal Water Allocation among Consumers. Water Resour Manage. 33: 3071–3093

43)    Zhang J, Zhu Z, Chang Y, Wu D, Du L, Cui Z. 2019. Demand Estimation of Water Resources based on Coupling Algorithm.  Chinese Control and Decision Conference (CCDC), Nanchang, China, 714-719.