Internet of Things-based ESP32 Mamdani Fuzzy Rainfall-risk Alert System with MATLAB Validation and Multichannel Alert Evaluation

Rainfall-related risks can disrupt agricultural activities, while conventional threshold-based warning systems may produce abrupt decisions near predefined boundaries and inadequately represent uncertainty among weather variables.In this study, we developed an Internet of Things (IoT)-based rainfall-risk alert system using a Mamdani fuzzy inference system implemented on a low-cost ESP32 platform.Hourly rainfall, relative humidity, and hourly atmospheric pressure change were used to classify conditions into Safe, Warning, and Danger levels.A publicly available weather dataset recorded at 10 min intervals was aggregated into 8780 complete hourly samples.Because only 67 samples belonged to the Danger class, a conditional Wasserstein generative adversarial network with gradient penalty (WGAN-GP) generated additional 6220 samples for rare-class and boundary-condition stress testing, producing a balanced dataset of 15000 samples.The ESP32 implementation reproduced the MATLAB reference without implementation-induced label differences across the evaluated datasets, indicating the consistent transfer of the Mamdani fuzzy model to a resource-constrained embedded platform.For the combined dataset, the defuzzified outputs produced a mean absolute error of 7.2458 × 10 -5 and a root mean square error of 1.3630 × 10 -4 .A conventional threshold-based baseline achieved 69.987% agreement and a Macro-F1 score of 69.801%.The active buzzer, Message Queuing Telemetry Transport (MQTT), and Telegram channels achieved 100% success during controlled experiments.These findings confirm the implementation consistency and communication feasibility of the proposed prototype, although field-level predictive validity requires further evaluation.