Flood Early Warning Systems Using IoT Telemetry: A Review of System Components and Applications

Flood disasters are increasing globally due to climate change, rapid urbanization, and unsustainable land use. Traditional flood monitoring approaches often fail to provide timely warnings, particularly in developing countries. The emergence of Internet of Things (IoT)-enabled Flood Early Warning Systems (FEWS) offers transformative potential by integrating real-time sensors, telemetry networks, cloud-based data processing, and multi-channel alert dissemination. This paper presents a systematic review of global and Kenya-specific applications of IoT telemetry in FEWS, highlighting system components, implementation strategies, challenges, and opportunities. Key components discussed include hydrological and meteorological sensors, communication technologies such as GSM, LoRaWAN, and satellite telemetry, data analytics and predictive models, and alert dissemination platforms. Lessons from pilot deployments in Kenya?s Tana River, Nyando, and Nairobi River catchments illustrate improvements in lead time, situational awareness, and community preparedness. Challenges such as connectivity gaps, high costs, and limited technical capacity are identified, alongside opportunities for low-cost, sustainable sensor networks and integration with predictive analytics. The study concludes with recommendations to enhance IoT-enabled FEWS in Kenya and similar developing countries, promoting resilient flood management infrastructure and informed disaster response.

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Publication Details

Journal
International Journal of Scientific Engineering and Research
Published
2025-10-03
DOI
https://doi.org/10.70729/se25918025150
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Flood Early Warning Systems Using IoT Telemetry: A Review of System Components and Applications

Abraham Nyete, Samuel M Nyaanga, Duncan Mbuge D
International Journal of Scientific Engineering and Research
Anomaly Detection Techniques and Applications
article

Flood Early Warning Systems Using IoT Telemetry: A Review of System Components and Applications

Abraham Nyete, Samuel M Nyaanga, Duncan Mbuge D
article en

Abstract

Flood disasters are increasing globally due to climate change, rapid urbanization, and unsustainable land use. Traditional flood monitoring approaches often fail to provide timely warnings, particularly in developing countries. The emergence of Internet of Things (IoT)-enabled Flood Early Warning Systems (FEWS) offers transformative potential by integrating real-time sensors, telemetry networks, cloud-based data processing, and multi-channel alert dissemination. This paper presents a systematic review of global and Kenya-specific applications of IoT telemetry in FEWS, highlighting system components, implementation strategies, challenges, and opportunities. Key components discussed include hydrological and meteorological sensors, communication technologies such as GSM, LoRaWAN, and satellite telemetry, data analytics and predictive models, and alert dissemination platforms. Lessons from pilot deployments in Kenya?s Tana River, Nyando, and Nairobi River catchments illustrate improvements in lead time, situational awareness, and community preparedness. Challenges such as connectivity gaps, high costs, and limited technical capacity are identified, alongside opportunities for low-cost, sustainable sensor networks and integration with predictive analytics. The study concludes with recommendations to enhance IoT-enabled FEWS in Kenya and similar developing countries, promoting resilient flood management infrastructure and informed disaster response.

International Journal of Scientific Engineering and Research
Openalex Percentile: Top 86%
Anomaly Detection Techniques and Applications
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Flood Early Warning Systems Using IoT Telemetry: A Review of System Components and Applications — Abraham Nyete, Samuel M Nyaanga, et al. · International Journal of Scientific Engineering and Research (2025) | TGRS Research Map | TGRS