# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 1 Introduction  
In recent years, the growing population and rising living standards have led to a significant increase in human demand for water resources. As a result, the amount of freshwater available has fallen below the critical threshold required for human survival [1]. Due to the extremely imbalanced distribution of water resources, the northern and northwestern regions of China are facing a freshwater emergency threat [2]. In recent years, ecological construction has been considered one of the most important factors in China's economic development, and water resource protection has become a top priority [3]. This is why there are currently so many water quality monitoring systems for lakes, rivers, and reservoirs. Water quality detection involves establishing a reasonable model through a series of analytical methods to identify patterns in water quality changes, allowing for the prediction of future trends [4, 5]. The water quality detection model can achieve risk warnings for water sources and improve the emergency response capability for sudden pollution [6]. The Menlou Reservoir in Yantai, China started constructing in 1958, and it was completed in 1960. The reservoir watershed area is over 1077 square kilometers. It is the main drinking water source supplying 70% water for the urban area of Yantai City. Inthis connection, the Menlou Reservoir performs an essential role in Yantai City [7]. The water quality of Menlou Reservoir is directly related to the development of the city and the life of residents. Therefore, the government attaches great importance to water quality monitoring in the reservoir and deployed a large number of sensors to obtain real-time water quality information. Many scholars and experts have also carried out research on the water quality monitoring in the Menlou Reservoir. [PERSON] et al. [8] proposed to design the Menlou Reservoir management information system platform based on the problems in the existing management system of Menlou Reservoir. Scholars have also completed a lot of research on the water quality of Menlou Reservoir. [PERSON] et al. [9] established a water quality prediction model for Menlou Reservoir when the water reached a moderate nutrient level in 2013 and proposed nutritive prevention and control measures suitable for Menlou Reservoir. The sensor group used to detect water quality information in Menlou Reservoir continuously transmits water quality indicators to the computer. This information has strong timeliness [10] characteristics, and its actual value is inversely proportional to time. Therefore, the water quality monitoring work must not only meet the objective requirements and standards for the quality of the results but also ensure real-time data processing [11]. If the traditional batch mode is used to process water quality indicators, it is impossible to grasp changes in the water body in time, resulting in lagging management measures. Stream processing, on the other hand, is a framework for receiving, processing, and outputting information in real-time, unlike the batch processing mechanism. Currently, with the abundance of water quality data, it is essential to establish appropriate models to analyze water quality in real-time to detect changes and predict future changes. To address various water quality issues, scholars have developed diverse water quality analysis models for practical solutions. [PERSON] et al. [12] employed the canonical correlation model and neural network model to study the primary factors affecting water quality changes in the Three Gorges Reservoir and developed a prediction model for water quality changes. [PERSON] et al. [13] tackled the limitations of traditional water quality detection models in processing high real-time high-frequency data and proposed a water quality big data processing platform that combined wavelet decomposition with the LSTM model. The above research provides guidance for reservoir water quality monitoring from various perspectives. [PERSON] et al.