Flood Detection System Using Data Fusion Technique for IoT

Abstract

Flood is a natural phenomenon that also occurs by a manmade pollution to cause climatic changes. They can result from heavy rainfall, snowmelt, storm surges, or the overflow of rivers and dams. Flood causes significant losses to people's lifestyles, as well as disrupt ecosystems and economies. Prevention and early warning systems are crucial for minimizing the impact of floods on communities. Flood detection involves using various technologies like sensors, satellite imagery, and machine learning algorithms to monitor water levels and detect anomalies indicative of flooding to provide early warnings and minimize damage. During floods, communities face property damage, loss of life, and displacement due to evacuation. Infrastructure damage disrupts rescue efforts and essential services, while health risks from contaminated water pose additional threats to affected populations. Economic impacts can be significant, requiring long-term recovery efforts to rebuild and restore affected areas. The existing system contains only monitoring the temperature, and humidity that is prediction based machine learning algorithms. To overcome this problem we proposed getting the required information for every individual who is staying in the particular zone. It is an easy communication between technology and people. To get sufficient details every individual can set their safety detection technique through mobile phones.