Deployment of Random Forest Algorithm for prediction of ammonia in river water
Document Type
Conference Proceeding
Publication Title
ACM International Conference Proceeding Series
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.
First Page
18
Last Page
23
DOI
10.1145/3651781.3651811
Publication Date
2-1-2024
Recommended Citation
Soumya, S.; Tamatgar, Nilufer; Dilli, Ravilla; and Kanthi, M., "Deployment of Random Forest Algorithm for prediction of ammonia in river water" (2024). Open Access archive. 10490.
https://impressions.manipal.edu/open-access-archive/10490
