Hydrogeochemical characterization based water resources vulnerability assessment in India's first Ramsar site of Chilka lake. (November 2022)

• Record Type: Journal Article
• Authors: Ruidas, Dipankar; Pal, Subodh Chandra; Saha, Asish; Chowdhuri, Indrajit; Shit, Manisa
• Is Part Of: Marine pollution bulletin. Volume 184(2022)
• Publication Date: 2022-11
• Subjects: VWRM Vulnerability of Water Resources Map -- ROC-AUC Receiver Operating Characteristics Area Under Curve -- VWR Vulnerability of Water Resources -- IDW Inverse Distance Weighted -- MLA Machine Learning Algorithm -- ANN Artificial Neural Network -- PPV Positive Predictive Values -- NPV Negative Predictive Values -- MDA Mean Decrease Accuracy -- BIS Bureau of Indian Standards -- LULC Land Use Land Cover -- VIF Variance Inflation Factor -- TOL Tolerance -- ML Machine Learning -- MC Multicollinearity -- RF Random Forest -- TP True Positive -- TN True Negative -- FP False Positive -- FN False Negative; Chilka lake -- Ramsar site -- Machine learning -- Anthropogenic stress -- Vulnerability; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2022.114107
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: A limnological site is significantly characterized by rich biological, chemical, and physical properties of the environment and is also described as the epitome of a large aquatic ecosystem. During the last few decades, the Chilka lake Ramsar site has experienced substantial degradation of water quality with associated deterioration of aquatic biodiversity. Our study aims to quantify the VWRM of the Chilka lake Ramsar region using the most reliable MLAs, namely ANN and RF, with the help of seventeen hydro-chemical properties of lake water. The produced map is validated through six validating measures (ROC-AUC- 0.89, Sensitivity-0.90, Specificity-0.78, PPV-0.78, NPV-0.88, Taylor diagram (r)-0.94), which depict that ANN is the most reliable ML algorithm in assessing the VWRM of the concerned region followed by RF. The prepared map of our study revealed that the eastern part was remarkably high to very high vulnerable zone covered area with 22.41 % and 7.19 %, respectively. Graphical abstract: Unlabelled Image Highlights: Vulnerability of water resources (VWR) in Chilka lake Ramsar site was assessed. Machine learning and neural network method was used to compared VWR. Anthropogenic activities are impacted on Chilka lake Ramsar wetland crisis. The effect of land use practices in VWR sustainability is critical.