## 1 Introduction  
Aquaculture is an important part of the fishery industry, and the quality of the aquaculture water directly affects the aquatic environment, yield and quality of aquatic products [1]. Currently China's aquaculture industry is still at the stage of rough development, fishery facilities and equipment and water quality management technology are relatively backward. Aquaculture water as an open, dynamic, variable and complex system, water quality is easily affected by many factors such as physical, chemical, biological and human activities [2]. According to statistics, the annual aquaculture economic loss caused by deterioration of aquaculture water quality in China is about 15 billion yuan [3]. Among them, the dissolved oxygen (DO) content in aquaculture water is one of the key factors for the survival of fish, which has an important impact on the feed consumption, energy consumption and metabolic rate of fish [4]. If the DO level is lower than 3 mg/L, it can easily lead to fish diseases and a large number of deaths [5]. In addition, the DO level in aquaculture is one of the most important parameters in water quality management [1]. The core issue for water quality DO management is to estimate future changes in the spatial and temporal patterns and development trends of water quality DO based on the monitored historical data, so as to provide a decision-making basis for preventing further deterioration of aquaculture water quality, realizing early warning and formulating water quality control measures [6]. Therefore, it is of great practical significance to carry out research on water quality DO prediction to promote scientific management of water quality and reduce aquaculture risk [7; 8]. At present, the research on the prediction method of DO content in aquaculture water quality has become a hotspot in the field of aquaculture, and the Internet of Things (IoT) technology, which is centered on online sensors, has made great progress in water quality monitoring applications [9]. However, due to such factors as easy equipment failure and human interference, the collected data suffer loss, invalidity, and noise interference, resulting in a lack of comprehensiveness and systematism of water quality samples [10]. Therefore, the Internet of Things technology is still in its infancy in the DO prediction of water quality. Usually, it can only carry out real-time monitoring and simple regulation of water quality and meteorological data through sensors, which prevents achieving accurate online prediction [11; 12]. In addition, because the water environment is a mixed system with multiple factors coexisting, in practical application the prediction of water quality DO content will be affected by the changes of many factors, such as ambient temperature, ambient air pressure, water depth, water temperature, wind speed, PH, and other factors in time and space, which may result in the abnormality of the predicted data [13; 14]. Therefore, in the prediction of water quality DO content, each environmental factor can be used as an independent variable to eliminate the randomness, synergistic phenomenon and coherent effect among the variables, and the algorithm can be used to establish a predictive model based on multiple independent variables, thus increasing the accuracy, reliability, and robustness of the prediction result [15]. In recent years, with the high-speed development of modern intelligent computing methods, domestic and foreign scholars have studied the use of existing real-time monitoring information of Internet of Things (IoT), big data analysis, artificial intelligence algorithms to solve problems related to the prediction of water quality DO. These DO prediction models can be summarized as: (1) the ecological dynamics model, based on traditional prediction methods such as the water quality simulation method, gray prediction, time-series analysis, and multiple regressions, etc.