Using support vector machine (SVM) technology to predict the duration of irrigation canal projects in western Iraq

AL-Somaydaii, Jumaa; Abdaljader, Aseel; Sharqi, Saadi Shartooh; Al Ansari, Nadhir

doi:10.22075/ijnaa.2023.7374

Using support vector machine (SVM) technology to predict the duration of irrigation canal projects in western Iraq

Document Type : Research Paper

Authors

Jumaa AL-Somaydaii ¹

Aseel Abdaljader ¹

Saadi Shartooh Sharqi ²

Nadhir Al Ansari ³

¹ Dams and Water Resources Department, University of Anbar, Ramadi, Iraq

² Civil Engineering Department, University of Anbar, Ramadi, Iraq

³ Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Sweden

10.22075/ijnaa.2023.7374

Abstract

In this study, a support vector machine (SVM) based technique for timing irrigation projects is presented, and one of the most accurate predictive models in calculating the final project duration within the contract documents, where the research problem is projects are not completed within the contract period because most of the total project duration is determined In an unthoughtful manner by the employer. Linear regression models were applied to data and information for several projects, and a significant improvement in forecast accuracy was obtained.

Keywords

linear regression

support vector machine

construction time

[1] R. Abd Karim, A.G. Abu and F.N.M. Khaja, Brainstorming approach and mind mapping in writing activity, Proc. English Educ. Inte. Conf. 1 (2016), no. 2, 423–429.

[2] M. Abdullah and N. Al-Ansari, Irrigation projects in Iraq, J. Earth Sci. Geotech. Eng. 11 (2021), no. 2, 35–160.

[3] J.A. Al-Somaydaii, Development mathematical model for brick works productivity by using support vector machine, Int. J. Appl. Eng. Res. 11 (2016), no. 23, 11126–11131.

[4] T.M.-A.-A. Al-Suwaidan and M.A. Al-Adlouni, Principles of creativity, Cordoba for Publishing and Distribution, 2004.

[5] D. Gough, J. Thomas and S. Oliver, Clarifying differences between review designs and methods, Syst. Rev. 1 (2012), no. 1, 1–9.

[6] A.A. Hameed and F.M.S. Al-Zwainy, Statistical evaluation of the planning and scheduling management process for irrigation and drainage projects, J. Algebra Statist. 13 (2022), no. 2, 259–282.

[7] S. Ray, Understanding support vector machine (SVM) algorithm from examples (along with code),

https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/, 2017.

[8] Saher, Forecasting of factors affecting brickwork productivity estimation by using artificial neural network, Thesis Master of Science in Civil Engineering, Baghdad University, 2014.

[9] Z.M. Yaseen, Z.H. Ali, S.Q. Salih and N. Al-Ansari, Prediction of risk delay in construction projects using a hybrid artificial intelligence model, Sustainability 12 (2020), no. 4, 1514.