Research Article
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Year 2023, Volume: 29 Issue: 1, 89 - 102, 31.01.2023
https://doi.org/10.15832/ankutbd.936335

Abstract

References

  • Ait-Kadi M, Abdellaoui R, Oulhaj A & Essafi B (1990). Design of large scale collective sprinkler irrigation projects for an on demand operation: a holistic approach. In Proceedings 14th International Congress on Irrigation and Drainage, Rio de Janeiro, Brazil. (Vol. 1, No. D, pp. 59-78).
  • Alandí PP, Álvarez JO & Martín-Benito JT (2007). Optimization of irrigation water distribution networks, layout included. Agricultural water management, 88(1-3), pp.110-118. doi: 10.1016/j.agwat.2006.10.004
  • Alperovits E & Shamir U (1977). Design of optimal water distribution systems. Water resources research, 13(6), pp.885-900. doi: 10.1029/WR013i006p00885
  • Arsene C T, Gabrys B & Al-Dabass D (2012). Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection. Expert Systems with Applications, 39(18), 13214-13224. doi:10.1016/j.eswa.2012.05.080
  • Beale M H, Hagan M T & Demuth H B (2014). Neural Network Toolbox User’s Guide (pp. 18–19). Natick, MA: The MathWorks Inc.
  • Calejo M J, Lamaddalena N, Teixeira J L & Pereira L S (2008). Performance analysis of pressurized irrigation systems operating on-demand using flow-driven simulation models. Agricultural water management, 95(2), pp.154-162. Doi: 10.1016/j.agwat.2007.09.011
  • Cantos W P & Juran I (2019). Infrastructure aging risk assessment for water distribution systems. Water Supply, 19(3), 899-907. Doi: 10.2166/ws.2018.139
  • Cunha M D C & Sousa J (1999). Water distribution network design optimization: simulated annealing approach. Journal of water resources planning and management, 125(4), pp.215-221. Doi: 10.1061/(ASCE)0733-9496(2001)127:1(69)
  • Czapczuk A & Dawidowicz J (2018). The Application of RBF Neural Networks for the Assessment of the Water Flow Rate in the Pipework. In 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018). Atlantis Press.
  • da Conceicao Cunha M & Ribeiro L (2004). Tabu search algorithms for water network optimization. European Journal of Operational Research, 157(3), pp.746-758. Doi: 10.1016/S0377-2217(03)00242-X
  • Dawidowicz J (2018). A Method for Estimating the Diameter of Water Pipes Using Artificial Neural Networks of the Multilayer Perceptron Type. In 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018). Atlantis Press. Doi: 10.2991/icaita-18.2018.13
  • Dawidowicz J, Czapczuk A & Piekarski J (2018). The application of artificial neural networks in the assessment of pressure losses in water pipes in the design of water distribution systems. Rocznik Ochrona Środowiska, (20), 292-308.
  • Garg V K & Bansal R K (2015). Comparison of neural network back propagation algorithms for early detection of sleep disorders. In 2015 International Conference on Advances in Computer Engineering and Applications (pp. 71-75). IEEE. Doi: 10.1109/ICACEA.2015.7164648
  • Geem Z W, Kim J H & Loganathan G V (2002). Harmony search optimization: application to pipe network design. International Journal of Modelling and Simulation, 22(2), pp.125-133. Doi: 10.1080/02286203.2002.11442233
  • Heaton J (2015). Introduction to Neural Networks for Java: Feedforward Backpropagation Neural Networks. Retrieved from http://www.heatonresearch.com/node/707.
  • Labye Y (1981). Iterative discontinuous method for networks with one or more flow regimes. In Proceedings of the international workshop on systems analysis of problems in irrigation, drainage and flood control. New Delhi, vol. 30, pp. 31-40.
  • Lamaddalena N (1997). Integrated simulation modeling for design and performance analysis of on-demand pressurized irrigation systems. Technical University of Lisbon, PhD, Dissertation. Portugal.
  • Lamaddalena N & Sagardoy J A (2000). Performance analysis of on-demand pressurized irrigation systems. No. 59. Food & Agriculture Org.
  • Lansey K E & Mays L W (1989). Optimization model for water distribution system design. Journal of Hydraulic Engineering, 115(10), pp.1401-1418. Doi: 10.1061/(ASCE)0733-9429(1989)115:10(1401)
  • Lertworasirikul S & Tipsuwan Y (2008). Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network. Journal of Food Engineering, 84(1), 65–74. doi:10.1016/j.jfoodeng.2007.04.019.
  • Maier H R, Simpson A R, Zecchin A C, Foong W K, Phang K Y, Seah H Y & Tan C L (2003). Ant colony optimization for design of water distribution systems. Journal of water resources planning and management, 129(3), pp.200-209. Doi: 10.1061/(ASCE)0733-9496(2003)129:3(200)
  • Moreno J J M, Pol A P, Abad A S & Blasco B C (2013). Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25(4), pp.500-506. Doi: 10.7334/psicothema2013.23
  • Mounce S R & Machell J (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. Doi: 10.1080/15730620600578538
  • Nazghelichi T, Aghbashlo M & Kianmehr M H (2011). Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Computers and Electronics in Agriculture, 75(1), 84–91. doi:10.1016/j.compag.2010.09.014. Doi: 10.1016/j.compag.2010.09.014
  • Omid M, Mahmoudi A & Omid M H (2009). An intelligent system for sorting pistachio nut varieties. Expert Systems with Applications, 36(9), 11528–11535. doi:10.1016/j.eswa.2009.03.040. Doi: 10.1016/j.eswa.2009.03.040
  • Pakalapati H, Tariq M A & Arumugasamy S K (2019). Optimization and modelling of enzymatic polymerization of ε-caprolactone to polycaprolactone using Candida Antartica Lipase B with response surface methodology and artificial neural network. Enzyme and microbial technology, 122, pp.7-18. Doi: 10.1016/j.enzmictec.2018.12.001
  • Priddy K L & Keller P E (2005). Artificial Neural Networks: An Introduction. Bellingham, Washington, USA: SPIE Tutorial Texts in Optical Engineering.
  • Schaake John C & Fu Hsiung L (1969). Linear programming and dynamic programming application to water distribution network design. MIT Hydrodynamics Laboratory.
  • Shirzad A & Safari M J S (2019). Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques. Urban Water Journal, 16(9), 653-661. Doi: 10.1080/1573062X.2020.1713384
  • Sigtia S & Dixon S (2014). Improved music feature learning with deep neural networks. In 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 6959-6963). IEEE.
  • Simpson A R, Dandy G C & Murphy L J (1994). Genetic algorithms compared to other techniques for pipe optimization. Journal of water resources planning and management, 120(4), pp.423-443.
  • Ucar M K, Nour M, Sindi H & Polat K (2020). The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets. Mathematical Problems in Engineering, 2020. Doi: 10.1155/2020/2836236
  • Wang W & Paliwal J (2006). Generalisation Performance of Artificial Neural Networks for Near Infrared Spectral Analysis. Biosystems Engineering, 94(1), 7–18. doi:10.1016/j.biosystemseng.2006.02.001. Doi: 10.1016/j.biosystemseng.2006.02.001

Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks

Year 2023, Volume: 29 Issue: 1, 89 - 102, 31.01.2023
https://doi.org/10.15832/ankutbd.936335

Abstract

Pressurized irrigation systems are widespread among other alternatives in Mediterranean countries. Since the initial investment costs of pressurized irrigation systems are quite high, it is crucial to determine design parameters such as pipe diameter. Most of the current optimization techniques for pipe diameter selection are based on linear, non-linear, and dynamic programming models. The ultimate aim of these techniques is to produce solutions to problems with less cost and computation time. In this study, a novel approach for determining pipe diameter was proposed
using Artificial Neural Networks (ANN) as an alternative to existing models. For this purpose, three pressurized irrigation systems were investigated. Different ANN architectures were created and tested using hydrant level parameters of the irrigation systems, such as irrigated area per hydrant, hydrant discharge, pipe length, and hydrant elevation. Different training algorithms, transfer functions, and hidden neuron numbers were tried to determine the best ANN model for each irrigation system. Using multilayer feed-forward ANN architecture, the highest coefficients of determination were found to be 0.97, 0.93, and 0.83 for irrigation systems investigated. It was concluded that pipe diameters could be determined by using artificial neural networks in the planning of pressurized irrigation systems. 

References

  • Ait-Kadi M, Abdellaoui R, Oulhaj A & Essafi B (1990). Design of large scale collective sprinkler irrigation projects for an on demand operation: a holistic approach. In Proceedings 14th International Congress on Irrigation and Drainage, Rio de Janeiro, Brazil. (Vol. 1, No. D, pp. 59-78).
  • Alandí PP, Álvarez JO & Martín-Benito JT (2007). Optimization of irrigation water distribution networks, layout included. Agricultural water management, 88(1-3), pp.110-118. doi: 10.1016/j.agwat.2006.10.004
  • Alperovits E & Shamir U (1977). Design of optimal water distribution systems. Water resources research, 13(6), pp.885-900. doi: 10.1029/WR013i006p00885
  • Arsene C T, Gabrys B & Al-Dabass D (2012). Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection. Expert Systems with Applications, 39(18), 13214-13224. doi:10.1016/j.eswa.2012.05.080
  • Beale M H, Hagan M T & Demuth H B (2014). Neural Network Toolbox User’s Guide (pp. 18–19). Natick, MA: The MathWorks Inc.
  • Calejo M J, Lamaddalena N, Teixeira J L & Pereira L S (2008). Performance analysis of pressurized irrigation systems operating on-demand using flow-driven simulation models. Agricultural water management, 95(2), pp.154-162. Doi: 10.1016/j.agwat.2007.09.011
  • Cantos W P & Juran I (2019). Infrastructure aging risk assessment for water distribution systems. Water Supply, 19(3), 899-907. Doi: 10.2166/ws.2018.139
  • Cunha M D C & Sousa J (1999). Water distribution network design optimization: simulated annealing approach. Journal of water resources planning and management, 125(4), pp.215-221. Doi: 10.1061/(ASCE)0733-9496(2001)127:1(69)
  • Czapczuk A & Dawidowicz J (2018). The Application of RBF Neural Networks for the Assessment of the Water Flow Rate in the Pipework. In 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018). Atlantis Press.
  • da Conceicao Cunha M & Ribeiro L (2004). Tabu search algorithms for water network optimization. European Journal of Operational Research, 157(3), pp.746-758. Doi: 10.1016/S0377-2217(03)00242-X
  • Dawidowicz J (2018). A Method for Estimating the Diameter of Water Pipes Using Artificial Neural Networks of the Multilayer Perceptron Type. In 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018). Atlantis Press. Doi: 10.2991/icaita-18.2018.13
  • Dawidowicz J, Czapczuk A & Piekarski J (2018). The application of artificial neural networks in the assessment of pressure losses in water pipes in the design of water distribution systems. Rocznik Ochrona Środowiska, (20), 292-308.
  • Garg V K & Bansal R K (2015). Comparison of neural network back propagation algorithms for early detection of sleep disorders. In 2015 International Conference on Advances in Computer Engineering and Applications (pp. 71-75). IEEE. Doi: 10.1109/ICACEA.2015.7164648
  • Geem Z W, Kim J H & Loganathan G V (2002). Harmony search optimization: application to pipe network design. International Journal of Modelling and Simulation, 22(2), pp.125-133. Doi: 10.1080/02286203.2002.11442233
  • Heaton J (2015). Introduction to Neural Networks for Java: Feedforward Backpropagation Neural Networks. Retrieved from http://www.heatonresearch.com/node/707.
  • Labye Y (1981). Iterative discontinuous method for networks with one or more flow regimes. In Proceedings of the international workshop on systems analysis of problems in irrigation, drainage and flood control. New Delhi, vol. 30, pp. 31-40.
  • Lamaddalena N (1997). Integrated simulation modeling for design and performance analysis of on-demand pressurized irrigation systems. Technical University of Lisbon, PhD, Dissertation. Portugal.
  • Lamaddalena N & Sagardoy J A (2000). Performance analysis of on-demand pressurized irrigation systems. No. 59. Food & Agriculture Org.
  • Lansey K E & Mays L W (1989). Optimization model for water distribution system design. Journal of Hydraulic Engineering, 115(10), pp.1401-1418. Doi: 10.1061/(ASCE)0733-9429(1989)115:10(1401)
  • Lertworasirikul S & Tipsuwan Y (2008). Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network. Journal of Food Engineering, 84(1), 65–74. doi:10.1016/j.jfoodeng.2007.04.019.
  • Maier H R, Simpson A R, Zecchin A C, Foong W K, Phang K Y, Seah H Y & Tan C L (2003). Ant colony optimization for design of water distribution systems. Journal of water resources planning and management, 129(3), pp.200-209. Doi: 10.1061/(ASCE)0733-9496(2003)129:3(200)
  • Moreno J J M, Pol A P, Abad A S & Blasco B C (2013). Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25(4), pp.500-506. Doi: 10.7334/psicothema2013.23
  • Mounce S R & Machell J (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. Doi: 10.1080/15730620600578538
  • Nazghelichi T, Aghbashlo M & Kianmehr M H (2011). Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Computers and Electronics in Agriculture, 75(1), 84–91. doi:10.1016/j.compag.2010.09.014. Doi: 10.1016/j.compag.2010.09.014
  • Omid M, Mahmoudi A & Omid M H (2009). An intelligent system for sorting pistachio nut varieties. Expert Systems with Applications, 36(9), 11528–11535. doi:10.1016/j.eswa.2009.03.040. Doi: 10.1016/j.eswa.2009.03.040
  • Pakalapati H, Tariq M A & Arumugasamy S K (2019). Optimization and modelling of enzymatic polymerization of ε-caprolactone to polycaprolactone using Candida Antartica Lipase B with response surface methodology and artificial neural network. Enzyme and microbial technology, 122, pp.7-18. Doi: 10.1016/j.enzmictec.2018.12.001
  • Priddy K L & Keller P E (2005). Artificial Neural Networks: An Introduction. Bellingham, Washington, USA: SPIE Tutorial Texts in Optical Engineering.
  • Schaake John C & Fu Hsiung L (1969). Linear programming and dynamic programming application to water distribution network design. MIT Hydrodynamics Laboratory.
  • Shirzad A & Safari M J S (2019). Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques. Urban Water Journal, 16(9), 653-661. Doi: 10.1080/1573062X.2020.1713384
  • Sigtia S & Dixon S (2014). Improved music feature learning with deep neural networks. In 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 6959-6963). IEEE.
  • Simpson A R, Dandy G C & Murphy L J (1994). Genetic algorithms compared to other techniques for pipe optimization. Journal of water resources planning and management, 120(4), pp.423-443.
  • Ucar M K, Nour M, Sindi H & Polat K (2020). The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets. Mathematical Problems in Engineering, 2020. Doi: 10.1155/2020/2836236
  • Wang W & Paliwal J (2006). Generalisation Performance of Artificial Neural Networks for Near Infrared Spectral Analysis. Biosystems Engineering, 94(1), 7–18. doi:10.1016/j.biosystemseng.2006.02.001. Doi: 10.1016/j.biosystemseng.2006.02.001
There are 33 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Ezgi Kurtulmuş 0000-0003-2535-2566

Ferhat Kurtulmuş 0000-0002-7862-6906

Hayrettin Kuşçu 0000-0001-9600-7685

Bilge Arslan 0000-0001-5550-2452

Ali Osman Demir 0000-0003-3409-6680

Early Pub Date January 18, 2023
Publication Date January 31, 2023
Submission Date May 11, 2021
Acceptance Date February 13, 2022
Published in Issue Year 2023 Volume: 29 Issue: 1

Cite

APA Kurtulmuş, E., Kurtulmuş, F., Kuşçu, H., Arslan, B., et al. (2023). Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks. Journal of Agricultural Sciences, 29(1), 89-102. https://doi.org/10.15832/ankutbd.936335

Journal of Agricultural Sciences is published open access journal. All articles are published under the terms of the Creative Commons Attribution License (CC BY).