Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: Application to Nitrate Contamination

Judeh, Tariq; Almasri, Mohammad N.; Shadeed, Sameer; Bian, Hanbing; Shahrour, Isam

doi:10.1134/s0097807822030162

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…The introduction of methods such as discriminant analysis, and more recently machine learning, has made a considerable contribution to the assessment and management (monitoring and surveillance) of groundwater resources [23]. Progress includes innovative applications of geographic information systems and statistical methods, improving contaminant management in particular [24,25]. The adoption of multivariate analysis and machine learning techniques, in particular ensemble learning [26,27], has improved the accuracy and efficiency of groundwater quality assessments, setting new benchmarks for robust and accurate classification in diverse regions.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning

Mohsine,

Kacimi,

Valles

et al. 2023

Hydrology

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In order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Côte d’Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies.

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Section: Introductionmentioning

confidence: 99%

Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning

Mohsine,

Kacimi,

Valles

et al. 2023

Hydrology

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“…A more comprehensive insight into specific vulnerability to nitrate pollution can be obtained by coupling multiple models and approaches. For example, in [32], the hydrological models HYDRUS-2D and crop-growth DSSAT were used to simulate water flow and nutrient leaching in potato farms [33], coupling with Geographic Information System (GIS), statistics and machine learning methods for both water quality assessment and prediction for the Eocene Aquifer, Palestine.…”

mentioning

confidence: 99%

The Nitrate Fate Tool: A Decision Support System for the Assessment of the Groundwater Vulnerability to Nitrate in Support of Sustainable Development Goals

Bancheri,

Basile,

Botta

et al. 2023

Sustainability

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This article introduces a new web-based and freely accessible tool, the Nitrate Fate tool (NFt), for the assessment of groundwater vulnerability to nitrate pollution in a variety of pedoclimatic conditions. The contamination of water resources by nitrate, in fact, represents a growing and persistent global environmental problem, and the utilization of practical tools to assist personnel working in the agricultural sector is key for mitigating the impact on land use, while maintaining farmers’ incomes. The (NFt) has been developed and integrated into the geospatial decision support system, LandSupport, as a way to support multiple stakeholders in conducting the so-called what-if scenario analysis (e.g., what would happen to the crop production if I substitute a quote of inorganic fertilizer with the same quote of an organic one?). The tool couples a state-of-art crop-growth model—which simulates crop growth dynamics, the nitrogen and carbon cycles—with a novel transfer function model in order to assess the transport of nitrate through the unsaturated zone to the groundwater table. Within the LandSupport platform, the results are shown both as coloured maps and as cumulative charts representing the travel times and the concentrations of root leachate to groundwater table depths. This work details the tool’s rationale, the coupling of the models, and their implementation. Moreover, this article shows examples of applications supporting several public authorities and end-users, underlining that, by combining all of the information on soils, groundwater table depths, management and climates, it is possible to obtain a comprehensive understanding of nitrogen transport dynamics. Two case studies are presented: the Piana del Sele and the eastern plain of Naples, both located in the Campania region of Italy. The results of the tool’s applications reveal significant groundwater vulnerability in both plains, mainly due to the shallow groundwater table depths, resulting in remarkably fast mean nitrate travel times ranging from 0 to 6 years. Finally, the tool provides a reproducible and replicable solution, and future implementation is foreseen for additional case studies all over the world.

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Potential risk assessment due to groundwater quality deterioration and quantifying the major influencing factors using geographical detectors in the Gunabay watershed of Ethiopia

Tegegne

Lohani

Eshete

2023

Environ Monit Assess

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Use of GIS, Statistics and Machine Learning for Groundwater Quality Management: Application to Nitrate Contamination

Cited by 8 publications

References 39 publications

Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning

Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning

The Nitrate Fate Tool: A Decision Support System for the Assessment of the Groundwater Vulnerability to Nitrate in Support of Sustainable Development Goals

Potential risk assessment due to groundwater quality deterioration and quantifying the major influencing factors using geographical detectors in the Gunabay watershed of Ethiopia

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