Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe

Safwan Mohammed, Sana Arshad, Bashar Bashir, Attila Vad, Abdullah Alsalman and Endre Harsányi

Agricultural Water Management, 2024, vol. 293, issue C

Abstract: Sodium hazard poses a critical threat to agricultural production globally and regionally which has been previously predicted from ground or surface water. Monitoring rainwater quality in this context is ignored but essential for agricultural water management in central Europe. Our study focused to predict sodium adsorption ratio (SAR) from 1985 to 2021 from ten ionic species of rainwater (pH, EC, Cl-, SO4−2, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) employing four machine learning (random forest (RF), gaussian process regression (GU), random subspace (RSS), and artificial neural network-multilayer perceptron (ANN-MLP)) methods at three stations K-puszta (KP), Farkasfa (FAK), and Nyirjes (NYR) of Hungary, central Europe. Exploratory data analysis was performed using the Mann-Kendall test, Pearson correlation, and principal component analysis (PCA). Rainwater composition revealed the highest percentage of SO4−2 ions i.e., 21 to 31%, followed by 10 to 15% of Na+ ions. Mann-Kendall test revealed a significant (p < 0.05) increasing trend of Na+ ions and SAR portraying it a serious hazard limiting agricultural production. Machine learning results from 10 model runs of all algorithms for SAR prediction at KP station proved the efficacy of ANN-MLP as superior with RMSE range of 0.02 to 0.05, followed by RF with RMSE of 0.14 to 0.19 in scenario 2 (SC-2) (Na+, Mg2+, Ca2+). Validation of the best-selected algorithm (ANN-MLP) and scenario (SC-2) also predicted the SAR with a low RMSE of 0.08 and 0.05 at both FAK and NYR stations, respectively. Hence, the efficiency of ANN-MLP in forecasting SAR from rainwater proves it to be a meticulous tool for enhancing agricultural water management practices in Central Europe and enhancing resource efficiency and crop production in the future.

Keywords: Rainwater chemistry; Sodium adsorption ratio; Multilayer perceptron; Agriculture water optimization; Hungary (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377424000258
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:293:y:2024:i:c:s0378377424000258

DOI: 10.1016/j.agwat.2024.108690

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().