Leveraging Internet of Things for Upstream and Downstream Real-Time Monitoring in Flood-Prone Areas: A Case Study in Brunei Darussalam

This study focuses and addresses the critical need for effective flood early detection and water quality assessment, particularly in regions prone to flooding in Brunei Darussalam. This is due to the alteration in weather patterns, commonly known as climate change. The research emphasizes the comprehensive understanding of the significance of pre-deployment characterization studies, which delve into various aspects such as hydrological patterns, water flow dynamics, sediment transport, and ecological conditions. This enables the development and optimal deployment of robust sensor networks and operation of Internet of Things (IoT) sensors capable of withstanding the environmental stressors by understanding the specific challenges posed by the river environment, such as variations in water levels, weather patterns and sediment concentrations. The implementation of IoT sensors in the river allows the collection and monitoring of real-time data on water quality parameters, water level, water flow dynamics and weather conditions. The use of mobile networks aims to transmit data through the gateway to display the data on a dashboard for visualization and generate an alert triggering an alarm for prompt evacuation and mitigation efforts. The integration of solar-powered IoT sensors with mobile networks offers a viable and sustainable approach for early flood detection and anomalies in water quality in river ecosystems leading to the improvement for community resilience and minimization of negative impacts floods have to infrastructure for efficient disaster management strategies around flood-prone regions in Brunei Darussalam.