Deployment of Random Forest Algorithm for prediction of ammonia in river water

S. Soumya, Nilufer Tamatgar, Ravilla Dilli, M. Kanthi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationICSCA 2024 - 2024 13th International Conference on Software and Computer Applications
PublisherAssociation for Computing Machinery
Pages18-23
Number of pages6
ISBN (Electronic)9798400708329
DOIs
Publication statusPublished - 01-02-2024
Event13th International Conference on Software and Computer Applications, ICSCA 2024 - Bali Island, Indonesia
Duration: 01-02-202403-02-2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Software and Computer Applications, ICSCA 2024
Country/TerritoryIndonesia
CityBali Island
Period01-02-2403-02-24

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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