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Groundwater quality forecasting using machine learning algorithms for irrigation purposes

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  • El Bilali, Ali
  • Taleb, Abdeslam
  • Brouziyne, Youssef

Abstract

Using conventional methods to evaluate the irrigation water quality is usually expensive and laborious for the farmers, particularly in developing countries. However, the applications of artificial intelligence models can overcome this issue through forecasting and evaluating the irrigation water quality indexes of aquifer systems using physical parameters as features. This paper aims forecasting the Total Dissolved Solid (TDS), Potential Salinity (PS), Sodium Adsorption Ratio (SAR), Exchangeable Sodium Percentage (ESP), Magnesium Adsorption Ratio (MAR), and the Residual Sodium Carbonate (RSC) parameters through Electrical Conductivity (EC), Temperature (T), and pH as inputs. To achieve this purpose, we developed and evaluated Adaptive Boosting (Adaboost), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) models using 520 samples of data related to fourteen Groundwater quality parameters in Berrechid aquifer, Morocco. The results revealed that the overall prediction performances of Adaboost and RF models are higher than those of SVR and ANN. However, the generalization ability and sensitivity to the inputs analyses show that the ANN and SVR models are more generalizable and less sensitive to input variables than Adaboost and RF. Globally, the developed models are valuable in forecasting the irrigation water quality parameters and could help the farmers and decision-makers in managing the irrigation water strategies. The developed approaches in this study have been revealed promising in low-cost and real-time forecast of groundwater quality through the use of physical parameters as input variables.

Suggested Citation

  • El Bilali, Ali & Taleb, Abdeslam & Brouziyne, Youssef, 2021. "Groundwater quality forecasting using machine learning algorithms for irrigation purposes," Agricultural Water Management, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:agiwat:v:245:y:2021:i:c:s0378377420321727
    DOI: 10.1016/j.agwat.2020.106625
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    References listed on IDEAS

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    1. Molle, François & Tanouti, Oumaima, 2017. "Squaring the circle: Agricultural intensification vs. water conservation in Morocco," Agricultural Water Management, Elsevier, vol. 192(C), pages 170-179.
    2. Simon Gosling & Nigel Arnell, 2016. "A global assessment of the impact of climate change on water scarcity," Climatic Change, Springer, vol. 134(3), pages 371-385, February.
    3. Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
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    Citations

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    1. Fatma Trabelsi & Salsebil Bel Hadj Ali, 2022. "Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia," Sustainability, MDPI, vol. 14(4), pages 1-23, February.
    2. Yu, Haijiao & Wen, Xiaohu & Wu, Min & Sheng, Danrui & Wu, Jun & Zhao, Ying, 2022. "Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture," Agricultural Water Management, Elsevier, vol. 262(C).
    3. Ahmed Khaled Abdella Ahmed & Mustafa El-Rawy & Amira Mofreh Ibraheem & Nassir Al-Arifi & Mahmoud Khaled Abd-Ellah, 2023. "Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region," Sustainability, MDPI, vol. 15(8), pages 1-16, April.
    4. De Angelis, Paolo & Tuninetti, Marta & Bergamasco, Luca & Calianno, Luca & Asinari, Pietro & Laio, Francesco & Fasano, Matteo, 2021. "Data-driven appraisal of renewable energy potentials for sustainable freshwater production in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.

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