Spatiotemporal assessment of groundwater quality and quantity using geostatistical and ensemble artificial intelligence tools

Nourani, Vahid; Ghaffari, Amirreza; Behfar, Nazanin; Foroumandi, Ehsan; Zeinali, Ali; Ke, Chang-Qing; Sankaran, Adarsh

doi:10.1016/j.jenvman.2024.120495

Cited by 2 publications

(1 citation statement)

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“…Precipitation serves as the essential mechanism for replenishing surface water and recharging groundwater through weathered and fractured zones, as outlined by (Pandey et al, 2023). This relationship between rainfall and groundwater is bolstered by the facilitation of water percolation through runoff, elucidating a strong positive correlation between the two, as noted by (Nourani et al, 2024). Over the span of two decades, rainfall data has been systematically collected from the SWDC, Gandhinagar, and utilized to generate a geographical distribution map employing the Inverse Distance Weighted (IDW) tool within the ArcGIS platform.…”

Section: Rainfallmentioning

confidence: 95%

Comparative Assessment of Analytical Hierarchy Process (AHP) and Fuzzy Overlay Analysis (FOA) Models in Groundwater Potential Zone Mapping Using Sensitive Analysis: A GIS-RS Integrated Approach

Gandhi,

Songara,

Prakash

et al. 2024

Preprint

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This study addresses the pressing need for assessing groundwater potential in diverse regions worldwide, emphasizing the challenges posed by industrialization and urban expansion. Leveraging remote sensing (RS) data within an ArcGIS framework, a comparison was conducted on the effectiveness of the Analytical Hierarchy Process (AHP) and Fuzzy Overlay Analysis (FOA) models in delineating groundwater potential zones. Ten influencing factors underwent rigorous multicollinearity checks to ensure independent analysis. Both methodologies categorized the output into five classification zones, revealing variations between methods. The sensitivity analysis identified specific influential layers for each method, showcasing heightened sensitivity in assessing groundwater potential zones. Notably, AHP emphasized factors such as NDWI, Lineament density, and Land Use Land Cover (LULC), while FOA prioritized Soil Texture, Lineament Density, and NDWI. Evaluating the reliability of groundwater potential assessments, AHP demonstrated fair agreement (59.18%), while FOA exhibited substantial agreement (77.5%). FOA emerged as superior, offering a more nuanced and precise representation of spatial relationships and uncertainties. The promising performance of FOA in our study suggests its potential applicability in accurate groundwater potential assessment beyond the study area (India) to other countries regions with heterogeneous hydrogeological conditions.

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Section: Rainfallmentioning

confidence: 95%