Authors: Ismail Gul – Stevens Institute of Technology
Title: Advancing the Monitoring of Pluvial Flood Inundation Using Low-Cost IoT-based Rainfall and Water Level Sensor Network
Presentation Type: Poster
Abstract: Improvement in urban early warning systems depends on timely and precise rainfall estimation. This work aims to develop an integrated framework based on measurements from a dual-purpose, low-cost Internet of Things-based sensor network to improve nowcasting and precipitation estimation performance and enable real-time data for actionable decision-making during extreme events like Hurricane Ida.
This study presents a novel urban flood monitoring network using observations from co-located water level and rainfall sensors. The high-density IoT sensor network is utilized to measure rainfall and Water level above ground at 1-minute intervals. This sensor network allows for real-time monitoring of local flood and rainfall events which is an improvement over the traditional high-water mark (HWM) observations. The proposed network has been operationally installed in two cities within New Jersey. The novelties of this work consist of a) using low-cost optical rainfall sensors deployed at the sub-kilometer scale to conduct spatial distribution analysis of rainfall events, b) evaluating the effectiveness of a co-located water level and rain sensors for flood monitoring in flood-prone city regions, and c) addressing the issue of overestimation of distance measurements due to temperature variations.
Future work will focus on expanding deployments state-wide to measure pluvial and fluvial flood and integrating temperature sensors for automated corrections.