Fuzzy Inference System-based Geo Visualization Tool Development for Yamuna River Water Quality Analysis
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Abstract
An analysis of Yamuna River Water Quality through the use of a Fuzzy Inference System illustrates the capabilities of artificial intelligence in the development of novel Geovisualizer. Numerous Geovisualizer systems exist worldwide, but none can estimate water quality with artificial intelligence instead, relying on statistical data that vary with the scenario and have dubious accuracy. This logic has not been applied to the classification of Water Quality Standards by any designated government agency for water quality monitoring and management. A robust and integrated application program interface is developed by combining MongoDB and JavaScript library utilities Leaflet.js, Node.js, ArcGIS, ERDAS Imagine, and Fuzzy Algorithm; embellished with HTML and CSS. The Yamuna River is considered for the spatial-temporal aspect of the application. End users can authenticate with JSON web tokens. Spatial and non-spatial data are visualized by Inverse Distance Weighted Interpolation technique of ArcGIS. The Water Quality Index can be calculated using combinations of critically chosen input parameters integrated through frontend and backend functionality that incorporates fuzzy set theory with input from a Geovisualizer database and frontend platforms; not available on the existing geovisualization platform. With its scalability, extensibility, and execution speed, the resultant 121RRwebgis Geovisualizer outperforms conventional applications. Thus, a robust platform for global researchers is developed.
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