Innovative Environmental Monitoring & Early Alert System

  • Unique Paper ID: 166787
  • Volume: 11
  • Issue: 2
  • PageNo: 1802-1805
  • Abstract:
  • This paper presents an innovative flood forecasting system integrating machine learning techniques such as k-Nearest Neighbors, logistic regression, decision trees, random forest classification, and ensemble learning. Analyzes data from weather stations, gauges, satellite imagery and GIS databases to improve accuracy and timeliness. A key feature is an SMS alert system sending real-time alerts to residents of flood-prone areas, providing information on risks, basic precautions and emergency contact numbers. This system aims to enhance disaster risk reduction efforts and community resilience.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{166787,
        author = {Harsh Goel and Gunjan Tripathi and Samiksha Arora and Priyanka Gupta and Dr. Tripti Sharma},
        title = {Innovative Environmental Monitoring & Early Alert System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {1802-1805},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166787},
        abstract = {This paper presents an innovative flood forecasting system integrating machine learning techniques such as k-Nearest Neighbors, logistic regression, decision trees, random forest classification, and ensemble learning. Analyzes data from weather stations, gauges, satellite imagery and GIS databases to improve accuracy and timeliness. A key feature is an SMS alert system sending real-time alerts to residents of flood-prone areas, providing information on risks, basic precautions and emergency contact numbers. This system aims to enhance disaster risk reduction efforts and community resilience.},
        keywords = {Flood prediction, Early warning system, Machine learning, SMS notification, Environmental monitoring, k-Nearest Neighbors (kNN), Logistic regression, Decision trees, Random forest classification, Ensemble learning, GIS database.},
        month = {July},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 2
  • PageNo: 1802-1805

Innovative Environmental Monitoring & Early Alert System

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