Design and implementation of a weather station for flood-prone agricultural lands

Abstract

One of the frequent natural hazards in Bangladesh is flooding which has a devastating impact on the agricultural productivity especially in the low-lying and rural areas. The absence of localized, real-time weather surveillance and the early notification of floods frequently causes a lot of crop loss, economic setbacks, and a decrease in livelihood of the agricultural people. This project will constitute the design and deployment of a low-cost, IoT based weather station that will be specifically designed to work on flood prone agricultural land. The system, proposed, incorporates the various environmental sensors to measure the significant parameters of rainfall, temperature, humidity, soil moisture, wind speed, atmospheric pressure, and water level in real-time. To process, store, and analyze data, data is sent via long-range and low-power LoRa communication network to a cloud-based platform. The prediction model application is based on machine learning to analyze real-time and historical data and estimate flood risks to provide early warnings. The system runs off a solar-powered energy management unit which has battery backup thus making it operate continuously in off-grid and remote regions. The system sends real-time alerts via SMS and a user-friendly web dashboard to the farmers. Experimental analysis proves to have credible data transmission, reasonable sensor accuracy, power-saving, and useful flood risk forecasting. The proposed solution provides a cost-effective, energy-efficient, and scalable solution to enhance flood preparedness, loss of crops, and sustainable agricultural practices in flood prone areas of Bangladesh.