Drought Forecast Model and Framework Using Wireless Sensor Networks

Taiwan faces a serious challenge with an increasing frequency of drought in recent years. Therefore, it is important to utilize the state-of-the-art sensing and communication technologies to monitor and forecast effectively the drought, and then notify the relevant departments for taking preventive measures against this natural disaster. This paper proposed and developed a Drought Forecast and Alert System (DFAS), which is a 4-tier system framework composed of Mobile Users (MUs), Ecology Monitoring Sensors (EMSs), Integrated Service Server (ISS), and Intelligent Drought Decision System (ID 2 S). DFAS combines the wireless sensor networks, embedded multimedia communications and neural network decision technologies to effectively achieve the forecast and alert of the drought. DFAS analyzes the drought level of the coming 7 th day via the proposed drought forecast model derived from the Back-Propagation Network algorithm. The drought inference factors are the 30-day accumulated rainfall, daily mean temperature, and the soil moisture to improve the accuracy of forecasting drought. These inference factors are detected, collected and transmitted in real-time via the Mote sensors and mobile networks. Once a region with possible drought hazard is identified, DFAS sends altering messages to users' appliances. System implementation results reveal that DFAS provide the drought specialists and users with complete environment sensing data and images. DFAS makes it possible for the relevant personnel to take preventive measures, e.g., the adjustment of agricultural water, for a reduced loss.