## 1 Introduction  
The rapid development of the Internet of Things has promoted the application of smart sensors in the field of the environment, contributing to big data and the multi-dimension characteristics of environmental monitoring [1, 2]. Outlier processing is critical in environmental data analysis owing to its significant effect on future analysis and modeling [3, 4]. Data quality control requires data mining algorithms, expert experience and Internet thinking [5, 6]. Meanwhile, the environment automatically requires monitoring values such as typical time series data, which have a large-scale collection time and include complex causes of outliers. At present, there are many ways to detect outliers in time series, such as outlier detection based on prior rules [7], statistical distribution characteristics [8], the Kalman Filter Model (KLM) and Bayesian model [9], the Generalised Linear Model (GLM) -based algorithm [10], intelligence algorithms [3], etc. Detection of time series outliers was first studied by [PERSON] (1972) [11, 12], who introduced two types of outliers, namely, additive outliers and innovations outliers. It is a common method to determine time series outliers through the residual analysis of ARMA model [13]. ARMA model requires that time series of analysis must conform to the hypothesis of stationarity, which is difficult to meet in practical application, such as meteorological, hydrological and environmental observation series data. The processing of non-stationary time series to stationary series can be realized by various mathematical transformation methods, such as difference transformation methods, mapping-based transformation methods, and splitting-based transformation methods [14], etc. With the development of datasets outlier detection methods, the detection methods can be divided into the following types: (a) distance-based outlier detection methods [15; 16]; (b) density-based outlier detection methods [17]; (c) clustering-based outlier detection methods [18]; (d) sliding window-based outlier detection methods [19]. Environmental monitoring is an important task in today's industrial production, environmental protection, ecology and global climate change research. Advanced and integrated environmental monitoring technology is quite necessary to solve complex ecological and environmental problems, such as remote sensing monitoring technology [20; 21; 22], Internet of Things technology [23] and soundscape ecology technology based on sound monitoring [24; 25]. The Chinese government has constantly attached great importance to the monitoring and control of environmental pollution [26]. With the deepening of pollution prevention and the great advances in monitoring technology in recent years, continuous automatic in situ monitoring has sent water environment monitoring into a new age. The capability of continuous monitoring in time and space makes environment monitoring technology an essential complementary method for field sampling, laboratory monitoring and analysis. The surface water quality automatic monitoring system is mainly composed of a surface water quality automatic monitoring station (referred to as a water station) and a water quality automatic monitoring data platform (referred to as a data platform). To date, more than 2000 national surface water quality automatic stations have been built in China. The constructions of large-scale water stations along with the advent of the \"Internet +\" big data era can meet the demand for higher environmental monitoring and management. With the increasing frequency of water quality monitoring, the amount of data that needs to be processed is growing. Integrating human experience and computer algorithms to effectively extract, select and process the acquired data, recognize the reliability of data, ensure the integrity of information, and reduce the uncertainty of data have become the key challenges which need solutions.