SURFACE WATER QUALITY MONITORING AND EVALUATION BASED ON WIRELESS SENSOR NETWORK

The environmental pollution caused by modern industrial and agricultural production is becoming more and more serious, especially water pollution, which has become a prominent problem restricting sustained economic development and hindering the improvement of people's living standards in many areas. Wireless sensor network can effectively overcome the shortcomings of traditional surface water quality monitoring methods such as poor real-time performance, high monitoring cost, and limited monitoring capabilities. It is very suitable for application in water quality monitoring systems and has become an important part of many water quality monitoring application platforms. In this study, we use the wireless sensor network to detect and evaluate the surface water quality. First, we build a reliable monitoring system using ZigBee wireless sensing theory for reliable data acquisition and transmission. After that, we design a T-S fuzzy neural network model suitable for water quality data processing. In order to verify the effectiveness of the T-S fuzzy neural network model, we select the water area near a certain city in the Yangtze River for data collection and model analysis. The experimental results show that the water quality evaluation results obtained by using the T-S fuzzy neural network model are consistent with the actual measured values (error less than 5%). The change trend of water quality over time predicted by the T-S fuzzy neural network model is consistent with the change trend of the actual measured value. In general, the T-S fuzzy neural network designed in this paper has met the design requirements for water quality evaluation.