A Modular LoRaWAN IoT Flood Monitoring with Support Vector Regression-Based Prediction System
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The challenge in developing a functional flood monitoring system is addressing the tradeoffs between cost-effectiveness, ease of implementation, and overall system functionality. This paper presents a low-cost LoRaWAN-based flood monitoring and prediction system (FMPS), utilizing modular and commercially available components with an integrated Support Vector Regression (SVR) flood prediction model. Calibration of the DPS310 pressure sensor was optimized using an iterative MATLAB program, which reduced the average sensor error to 0.5772 kPa. The enclosure bias test and indoor versus outdoor deployment tests revealed a negligible pressure reading difference ranging from 0.1 to 0.2 kPa, while data transmission testing showcased a 0.04% server-side decoding error. Finally, the SVR model achieved a high correlation coefficient (R2 = 0.9477), a low Mean Absolute Error (MAE = 0.0512), and a prediction validity of 1.5 hours in advance, showcasing its effectiveness. These results highlight the system’s reliability and prediction accuracy compared to other monitoring systems, therefore providing a scalable and cost-effective solution for accelerating emergency response times during intense flooding scenarios.
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