ReviewEnvironmental geochemistry and health2025
Surface water quality evaluation impacting drinking water sources and sanitation using water quality index, multivariate techniques, and interpretable machine learning models in Mahanadi River, Odisha (India).
Review in Environmental geochemistry and health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Assam's Digaru River under threat: Integrated water quality and human health risk assessment of an industry-laden system through the lens of multivariate statistics and water quality indexing.Environmental geochemistry and health · 2026Article
- An efficient uncertainty integrated aggregation scheme for water quality detection and longitudinal carbon offset estimation in the Yamuna River.Scientific reports · 2026Article
- Analysis of river water quality in Rourkela Odisha using multiple indices to inform sustainable water management.Scientific reports · 2026Article
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Authors and funding
1 author.
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Abstract
Water quality and quantity affect crop productivity, with surface water quality having a significant impact. The amount of surface water being used for drinking is gradually rising. Thus, assessing surface water quality and related hydro-chemical characteristics is essential for surface water resource management in Mahanadi River Basin, Odisha. The current study examined surface water quality and appropriateness for drinking and agriculture, utilizing several techniques such as Weighted Arithmetic (WA) Water Quality Index (WQI), Multivariate models namely Pearson Correlation, Cluster Analysis (CA) and Principal Component Analysis (PCA), six multiple machine learning (ML) techniques like, gaussian process regression (GPR), linear regression (Stepwise), fit binary tree (FBT), support vector regression, SVM (linear and polynomial kernels), and artificial neural network (ANN) to predict the WQI, for sustainable use of the surface water resources. Thirteen physicochemical parameters were used to analyse eleven surface water samples, which indicating that the primary cation and anion concentrations were as follows: Mg
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