CROWDSOURCED LOCAL ISSUE REPORTING AND RISK MAPPING WITH POWER BI

Public infrastructure management remains a significant challenge for people living in both urban and rural areas, where traditional complaint systems often suffer from inefficiencies, delays, and lack of transparency. This paper presents a comprehensive survey of existing Artificial Intelligence (AI)-based complaint management systems and evaluates their limitations in terms of automation, accuracy, and user engagement. Various approaches, including Natural Language Processing (NLP), machine learning models, and geolocation-based systems, are reviewed and analyzed. Based on the identified research gaps, this paper proposes an AI-powered complaint management system that integrates text and image classification with GPS-based location intelligence. The proposed system aims to improve complaint classification accuracy, ensure efficient routing to appropriate authorities, and enhance transparency through real-time tracking. This study contributes by addressing the limitations of existing systems and presenting a scalable solution that benefits citizens across both urban and rural environments.

@article{ijter2026212604217775,
  author = {Mr. Swaradh P and RAHUL R and Muhammed Zidhan K K and Muhammad Sinan K T and Rasitha R},
  title = {CROWDSOURCED LOCAL ISSUE REPORTING AND RISK MAPPING WITH POWER BI},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2026},
  volume = {2},
  number = {4},
  pages = {211-215},
  doi =  {10.64823/ijter.2604024},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212604217775/crowdsourced-local-issue-reporting-and-risk-mapping-with-power-bi},
  abstract = {Public infrastructure management remains a significant challenge for people living in both urban and rural areas, where traditional complaint systems often suffer from inefficiencies, delays, and lack of transparency. This paper presents a comprehensive survey of existing Artificial Intelligence (AI)-based complaint management systems and evaluates their limitations in terms of automation, accuracy, and user engagement. Various approaches, including Natural Language Processing (NLP), machine learning models, and geolocation-based systems, are reviewed and analyzed. Based on the identified research gaps, this paper proposes an AI-powered complaint management system that integrates text and image classification with GPS-based location intelligence. The proposed system aims to improve complaint classification accuracy, ensure efficient routing to appropriate authorities, and enhance transparency through real-time tracking. This study contributes by addressing the limitations of existing systems and presenting a scalable solution that benefits citizens across both urban and rural environments.},
  keywords = {Artificial Intelligence (AI), Natural Language Processing (NLP), Global Positioning System (GPS), Machine Learning (ML), Long Short-Term Memory (LSTM), AutoRegressive Integrated Moving Average (ARIMA)},
  month = {Apr},
}

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