Internet of Things early flood warning system with ethology input and fuzzy logic

Abstract

Flood is considered as a serious natural disaster in Asia. Flood has affected millions of people in Asia in the recent years including Malaysia and its neighboring countries. The severity of the problems resulted from flood has significantly affected the government in terms of economic and social. Information Communication Technology (ICT) can be utilized in addressing flood challenge by contributing in the aspects of early flood warning as well as alerting the affected community. Early flood warning systems face several challenges in terms of warning dissemination that is not timely, people centered, accessible and explainable. Thus, this study developed an Internet of Thing (IoT) early flood warning system (IEFWS) with ethological input using fuzzy logic in order to come up with a timely, precise and low cost flood warning system. The IEFWS of fuzzy logic application included several nature input data membership functions specifically temperature, humidity, rainfall intensity, water raise rate, sound, and motion indicators were all being updated on the internet simultaneously in less then 0:00:05 seconds. This study also included an ethological input data of fish by analyzing the behavior of sound and movement of fish as indicators to early warning before flood occurrence. The system was tested and evaluated in terms of timely and preciseness of it to update sensor data to the internet and apply fuzzy logic to intelligently alert flood warning. The results showed that the system was able to update ubiquitous data for a better monitoring system platform. In addition, the system is low cost and easy to handle. In conclusion, the IoT early flood warning system is timely and precise as the data are updated at a very minimum delay and it could easily monitor the changes of climate.