Open Access

ARTICLE

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

Rab Nawaz Bashir¹, Imran Sarwar Bajwa², Muhammad Waseem Iqbal^3,*, 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. https://doi.org/10.32604/iasc.2023.030844

Received 03 April 2022; Accepted 29 May 2022; Issue published 05 January 2023

Abstract

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 (ET_c) rate. ML-assisted ET_c is proposed to adjust the LF according to the ET_c and soil salinity level. A regression model is proposed to determine the ET_c rate according to the prevailing temperature, humidity, and sunshine, which would be used to determine the smart LF according to the ET_c and soil salinity level. The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration (ET_o) 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.

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Keywords

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