Deployment of Random Forest Algorithm for prediction of ammonia in river water
Pages 18 - 23
Abstract
The fascinating aspect of machine learning (ML) is its diverse application. ML models are most useful when it comes to the conservation of natural resources through sustainable usage. An essential natural resource, water is vital to life as we know it. Ammonia poses a serious hazard to aquatic life and is a primary source of pollution in waterways. To estimate the ammonia content in river waters, machine learning algorithms are used in this study. After testing and training many ML regression models, The Flask API is used to deploy the model that fits the data the best. Based on the values of pH, DO (dissolved oxygen), and COD (chemical oxygen demand), the website shows the amount of ammonia in the river water.
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Published In
February 2024
395 pages
ISBN:9798400708329
DOI:10.1145/3651781
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
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Published: 30 May 2024
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ICSCA 2024
ICSCA 2024: 2024 13th International Conference on Software and Computer Applications
February 1 - 3, 2024
Bali Island, Indonesia
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