Groundwater quality forecasting using machine learning algorithms for irrigation purposes

By Ali el Bilali, A. Taleb, Youssef Brouziyne. 2020

295 citations recorded by the source corpus.

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.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/4dc1e5aa53bb79409507e491c9891f1a92711da0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Mengyuan Zhu
2. Hongqiang Ren
3. Jiawei Wang
4. Xiao Yang
5. Lin Ye
6. Bin Wu
7. Linyu Zhang

In Water Science and Technology

1. Mengyuan Zhu
2. Hongqiang Ren
3. Jiawei Wang
4. Xiao Yang
5. Lin Ye
6. Bin Wu
7. Linyu Zhang

Citations in the corpus

• A review of the application of machine learning in water quality evaluation (2022, methodology): “Agrawal et al. [46] estimated and predicted the WQI of groundwater using a combination of particle swarm optimization (PSO) and SVM, which illustrated the feasibility of integrating these methods for groundwater prediction [47].”
• Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Effective water management can also lead to the improvement of water quality as well as reduction of pollution and health risks [66].”