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Abstract

The Climate is becoming increasingly unpredictable, while the incidence of extreme weather is on the rise — both are contributing to surging global demand for advanced flood forecasting and monitoring services. This paper introduces an AI-powered Flood Monitoring and Warning System (FMWS) through an IoT sensor network, scalable real-time data analytics, and Machine Learning (ML) models to enhance the accuracy of prediction, risk analysis, and early warning dissemination in the notified areas. Hydrological and meteorological data would be collected by an ultrasonic sensor, a radar sensor, and a pressure sensor interfacing via GSM/GPRS, Wi-Fi, LoRa, or satellite network links. Machine learning models such as Random Forest and Neural Networks are used for detecting flood patterns and have achieved a prediction accuracy of 98%, 95% precision, recall of 95%, and a false alarm ratio of 3%. Outlet works for intermediate elevations were tested with a synthetic flood series and historical hydrological data cross-referencing, and the system showed performance in different scenarios. Comparative tests against accommodating the other systems (Logistic Regression and ARIMA in the baseline) demonstrated effectiveness in prediction accuracy, response liveness, and dynamic adaptability. In addition, the system also sends SMS, email, or mobile app alerts during emergencies and includes graphical representations, historical data, and multi-channel dashboards with data from the Flood Management and Warning System (FMWS) for an intuitive experience. The integration of IoT, AI, and geospatial techniques into a single flood management system is novel. It can also save money by preventing delays in response to disasters, thereby increasing flood resilience. These results are derived from the FMWS, which, powered by AI, assists with governance and contemporary disaster risk management planning.

Reason for Retraction

This article has been retracted at the request of the Editorial Office, following an internal investigation conducted in accordance with the Committee on Publication Ethics (COPE) Retraction Guidelines.

The investigation identified serious concerns affecting the integrity and reliability of the published work. Specifically, one or more of the following issues were confirmed:

  1. Undisclosed use of computer-generated text and/or data, in which substantial portions of the content were produced using algorithmic or artificial intelligence–based tools without transparent disclosure, contrary to the journal's authorship and transparency policies.

  2. Compromised peer-review process, indicating irregularities that undermine the validity, independence or authenticity of the review procedure.

  3. Inappropriate or misleading citations, including references that are irrelevant, improperly used, or appear to artificially inflate citation metrics, thereby distorting the scholarly record.

  4. Authorship-related concerns, including the addition of new author(s) at a later stage of the publication process without adequate justification, documentation, or transparent disclosure, raising unresolved questions regarding author contributions, responsibility, and compliance with the journal's authorship criteria.

The Editorial Office determined that these issues significantly compromise the scientific integrity of the article, and that correction alone would be insufficient to address the concerns. Retraction was therefore deemed necessary to maintain the accuracy and trustworthiness of the scholarly record.

The authors were informed of the findings and the retraction decision. While the authors do not respond to this retraction, the journal has proceeded with the retraction in line with COPE guidance, which permits retraction without author consent when editorial integrity is at risk.

This retraction is issued to alert readers that the findings and conclusions of the article should not be relied upon. The original article will remain accessible for the sake of the scholarly record, but it will be clearly marked as retracted.

Apologies are offered to readers of the journal that this was not detected during the submission process.

Please see the Retraction Notice available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss4/8

DOI: https://doi.org/10.52866/2788-7421.1354

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