A REVIEW ON FLOOD DETECTION SYSTEMS USING INTERNET OF THINGS: TECHNIQUES, COMPARATIVE ANALYSIS AND FUTURE DIRECTIONS

Abstract

Floods are among the most frequent and destructive natural disasters, and the ability to detect rising water levels early is critical for reducing loss of life and property. Over the past several years, the Internet of Things (IoT) has emerged as a dominant technological approach for automated, real time flood monitoring, replacing slow and labour-intensive manual observation methods. This paper presents a structured review of IoT-based flood detection systems reported in the literature between 2018 and 2025. The reviewed works are classified into five broad categories — threshold based sensor systems, machine-learning/AI-based predictive systems, cloud-integrated systems, GSM-based low-connectivity systems, and wireless-sensor-network (WSN) based systems — and are compared in terms of sensing technique, connectivity, and alerting mechanism. A low-cost ESP32-based prototype combining an ultrasonic sensor, float sensor, rain sensor and temperature humidity sensor with the Blynk IoT platform is discussed as an illustrative case study within the threshold-based category. Based on the comparative analysis, this review identifies key open challenges, including limited predictive capability in threshold-based designs, dependency on internet connectivity in cloud-based designs, energy constraints in WSN deployments, and the general absence of large-scale field validation across the surveyed works. The paper concludes by outlining promising future research directions, including hybrid sensor-AI architectures, energy efficient long-range communication, and integration with geographic information system (GIS) based regional flood-risk mapping.