Abstract
In the present study, simultaneous management of water and wastewater was examined using ant and artificial neural network algorithms. Ant algorithm is one of the most meta-heuristic algorithms to solve optimization problems. The data were collected in a monthly time series from the Regional Water Organization of Khorasan Razavi during 1998–2016. Water flow data were initially estimated in the study. In this regard, different structures of the back propagation network (1, 2, 3, 4 and 5 nodes in the hidden layer) were designed by using logistic activation function in order to evaluate the efficiency of ANN model and its comparison with the ARIMA method in predicting the time series of the flows for the time horizons of next 3, 6, 9 and 12 month. The output data of each network were finally compared with actual data to evaluate the efficiency of the model and its comparison with ARIMA model using the evaluation criteria of models. Water resources were, then, allocated to drinking, agricultural and industrial sectors using the ant algorithm. According to the results of the total consumption of drinking water for the industrial sector, the southeast and northwest of Mashhad have the greatest amount of water. In other words, the northeastern and southwestern parts of Mashhad have the least amount of water for the industrial sector. And so, there must be a higher priority to water scarcity in these two regions than the other areas.
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References
Aliabad FA, Shojaei S, Zare M, Ekhtesasi MR (2018) Assessment of the fuzzy ARTMAP neural network method performance in geological mapping using satellite images and Boolean logic. Int J Environ Sci Technol 1–10
Azadnia A, Zahraei B (2010) Application of PSO optimization algorithm in optimizing multi-objective exploitation of dams’ reservoirs. In: The 5th National Congress of Civil Engineering, Ferdowsi University of Mashhad
Barikhani A, Ahmadian M, Khalilian S, Chizari AH (2012) Sustainable consolidated use of surface water and groundwater resources in determining the optimal pattern of cultivation in Qazvin plain. Q J Agric Econ Dev 77:29–56
Bullnheimer B, Hartl RF, Strauss C (1999) A new rank-based version of the ant system: a computational study. Cent Eur J Oper Res Econ 7(1):25–38
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperaive learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66
Gikas P, Tchobanoglous G (2009) The role of satellite and decentralized strategies in water resources management. J Environ Manag 90:144–152
Golestan A, Molaie Zahedi Z (2012) The investigation of group-based intelligence optimization algorithms. In: Eighth symposium on advances in science and technology, Mashhad
Imranpour H, Taherpour H, Ebadzadeh MM (2008) Optimization algorithm of two-stage particles group. In: Fourteenth annual conference of computer society of Iran, Tehran
Jitea LM (2011) Appropriate methods for evaluating the agricultural policies’ consequences at farm level. Not Bot Hort Agrobot Cluj 39(1):126–133
Karamoze M, Ahmadi A, Nazif S (2006) Challenges and opportunities of using optimal utilization models of water resources systems. In: The first regional conference on optimal utilization of water resources in Karoon and Zayandeh Rood Basins, ShahreKord
Kumar DN, Reddy MJ (2006) Ant colony optimization for multi-purpose reservoir operation. J Water Resources Manag 20(6):879–898
Li X, He J, Liu X (2009) Intelligent GIS for solving high dimensional site selection problems using ant colony optimization techniques. Int J Geogr Inf Sci 23(4):399–416
Liu SF, Konstantopoulou P, Gikas L, Papageorigou G (2011) A mixed integer optimization approach for integrated water resources management. Comput Chem Eng 35:858–868
Liu S, Papageorgiou LG, Gikas P (2012) Integrated management of non-conventional water resource in Anhydrous Islands. Water Resourse Manag 26:359–375
Maier HR, Simpson AR, Zecchin AC, Foong WK, Phang KY, Seah HY, Tan CL (2003) Ant colony optimization for design of water distribution system. J Water Resource Plan Manag 129(3):200–209
Medellin-Azuara J, Howitt RE, Harou JJ (2012) Predicting farmer responses to water pricing, rationing and subsidies assuming profit maximizing investment in irrigation technology. Agric Water Manag 108:73–82
Sekulić Z, Antanasijević D, Stevanović S, Trivunac K (2017) Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process. Int J Environ Sci Technol 14(7):1383–1396
Shojaei S (2016) Evaluation of pH and EC changes in wastewater application in different depths of soil. J Res Ecol 4(2):340–346
Shojaei S, Shojaei S (2017) Experimental design and modeling of removal of Acid Green 25 dye by nanoscale zero-valent iron. Euro-Mediter J Environ Integr 2(1):15
Shojaei S, Shojaei S, Sasani M (2017) The efficiency of eliminating Direct Red 81 by Zero-valent Iron nanoparticles from aqueous solutions using response surface Model (RSM). Model Earth Syst Environ 3(1):27
Simpson AR, Maier HR, Foong WK, Phang KY, Seah HY, Tan CL (2001) Selection of parameters for ant colony optimization applied to the optimal design of water distribution systems. In: Proc., int. congress on modeling and simulation, Canberra, Australia, pp 1931–1936
Stutzle T, Hoos HH (2000) Max–min ant system. Future Gener Comput Syst 16(9):889–914
Zecchin AC, Maier HR, Simpson AR, Leonard M, Nixon JB (2007) Ant colony optimization applied to water distribution system design: comparative study of five algorithms. J Water Resources Plann Manag 133(1):87–92
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See Tables 22, 23, 24, 25, 26, 27 and 28.
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Rastegaripour, F., Saboni, M.S., Shojaei, S. et al. Simultaneous management of water and wastewater using ant and artificial neural network (ANN) algorithms. Int. J. Environ. Sci. Technol. 16, 5835–5856 (2019). https://doi.org/10.1007/s13762-018-1943-0
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DOI: https://doi.org/10.1007/s13762-018-1943-0