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Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

Rab Nawaz Bashir1, Imran Sarwar Bajwa2, Muhammad Waseem Iqbal3,*, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7

1 Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Pakistan
2 Department of Computer Science and IT, Islamia University Bahawalpur, Pakistan
3 Department of Software Engineering, Superior University Lahore, 54000, Pakistan
4 Department of Computer Science, GC Women University, Sialkot, 53310, Pakistan
5 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, 21493, Saudi Arabia
6 Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
7 Department of Computer Science, College of Computing in Al-Qunfudah, Umm Al-Qura University, Makkah, 24381, Saudi Arabia

* Corresponding Author: Muhammad Waseem Iqbal. Email:

Intelligent Automation & Soft Computing 2023, 36(2), 1915-1930.


Soil salinity is a serious land degradation issue in agriculture. It is a major threat to agriculture productivity. Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction (LF) of irrigation water. For the leaching process to be effective, the LF of irrigation water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration (ET) rate. The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning (ML) based decisions are required to determine the calibrated Evapotranspiration (ETc) rate. ML-assisted ETc is proposed to adjust the LF according to the ETc and soil salinity level. A regression model is proposed to determine the ETc rate according to the prevailing temperature, humidity, and sunshine, which would be used to determine the smart LF according to the ETc and soil salinity level. The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration (ETo) determination. The validation of the model from the test dataset reveals the accuracy of the ML model in terms of Root mean squared errors (RMSE) are 0.41, Mean absolute errors (MAE) are 0.34, and Mean squared errors (MSE) are 0.28 mm day−1. The applications of the proposed solution in a real-time environment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.


Cite This Article

R. N. Bashir, I. S. Bajwa, M. W. Iqbal, M. U. Ashraf, A. M. Alghamdi et al., "Leaching fraction (lf) of irrigation water for saline soils using machine learning," Intelligent Automation & Soft Computing, vol. 36, no.2, pp. 1915–1930, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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