Flooding Forecasting System Based on Water Monitoring with IoT Technology

Floods are natural catastrophes that impact negatively on natural life, farming, business, and infrastructure each year. Different hydrological and climatic variables impact flooding. Several studies on flood catastrophe management and food forecasting systems have been performed. However, with the assistance of recent technology developments, it is now critical to transition from individual tracking and forecasting frameworks to knowledgeable flood forecasting processes that support stakeholders and floods that effect everybody equitably. The Internet of Things (IoT) is a solution that utilizes embedded device hardware with a wireless communication network to transmit data from sensors to a computing device for real-time analysis. Flood forecasting attention has turned ahead from mathematical or hydrological concepts toward the algorithmic methodologies. Flood data is non-linear and changeable in character. Artificial neural networks and other approaches are utilized to create prediction systems for floods. The flooding forecasting system proposed in this study uses IoT and artificial neural networks. With a dashboard display, the web-based tool is designed to monitor the possibility of local flooding. The system outperforms the competition in terms of prediction accuracy.