Abstract
Due to increased industrialization and human density, the Ganga are becoming one of the most polluted rivers in the world. As a result, the Water Quality Index(WQI) for river water is calculated to check the water quality. The Central Pollution Control Board (CPCB), an Indian organization, built several monitoring stations to keep an eye on the values of the physicochemical parameters under consideration. The Ganga river’s water quality index will be developed utilizing eight physicochemical parameters. We have utilized a Linear Regression technique to estimate the trends and quality of the water for the following five years based on the trend observed over the last ten years, from 2011 to 2020. The properties of the provided dataset were then categorized and rated using the Decision Tree Method and Random Forest algorithm. The results of the algorithms were scaled with grades ranging from Excellent (A) to Very Bad (E). Decision trees and random forests are powerful machine-learning algorithms that can be used for regression and classification tasks. Further, we evaluated the two classification algorithms for accuracy-related performance. It can be seen that the two algorithms and the Random Forest algorithm give more accurate results. On the other hand, The Linear Regression algorithm gave alarming results for the river Ganga as water quality was deteriorating over the years. The Ganga river’s declining water quality index sparked widespread concern, prompting quick responses from the general public and individuals who had raised their awareness and consciousness.
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Bijalwan, Y., Chaudhari, P., Sharma, O., Raghavendra, S. (2023). Analysis and Prognosis of Water Quality for River Ganga Using Water Quality Index. In: Prabhu, S., Pokhrel, S.R., Li, G. (eds) Applications and Techniques in Information Security . ATIS 2022. Communications in Computer and Information Science, vol 1804. Springer, Singapore. https://doi.org/10.1007/978-981-99-2264-2_15
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DOI: https://doi.org/10.1007/978-981-99-2264-2_15
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