A Hybrid Prediction Model for Monitoring of River Water Quality in the USN System

River water quality is directly related to the wellness of its neighbors. Because the West Nakdong River has long suffered both from the infiltration of sea water and from the inflow of turbid wastewater, inconsiderate use of this water can cause disastrous result to nearby agricultural areas and neighbors. Busan city in Korea had deployed a pilot USN (ubiquitous sensor network) system that monitors this river and nearby tube wells to properly react to those situations. In this paper, we have designed a system that predicts salinity level of groundwater while monitoring the electrical conductivity (EC) values of sensors in that USN. We use a hybrid method that combines pattern-based approach together with statistical regression model to analyze sensor data. After classifying past sensor outputs into several characteristic patterns, we trace each day's change to identify base pattern of that day and thus predict the next value of sensor output. Since the detection of each day's pattern takes some time, we need to incorporate statistical regression model as an interim prediction method. Through an experiment that compares the hybrid model to previous statistical regression model, we have shown that our hybrid model is more accurate to predict the sensor's movement.