###### Abstract
Moreover, with increased freshwater discharge during the summer season, there is an increase in the abundance of species that cause red tides [15; 16]. In general, phytoplankton are affected by bottom-up control, involving nutrients, radiation intensity, water temperature, salinity, and physical circulation (advection and diffusion), and top-down control, which includes predation [17; 18]. However, such factors exhibit complex characteristics in an estuary due to the freshwater influx from the river and seawater circulation by tidal forcing. Tidal forcing leads to high-frequency (diel and fortnightly) variations of the estuarine phytoplankton contents, whereas freshwater discharge produces low-frequency patterns, that is, seasonal and annual variations [19; 20]. In an altered estuary, such as the YR estuary, where freshwater inflow is intermittent, artificial, and unpredictable, phytoplankton may vary in an irregular, high-frequency, and transient fashion rather than in a regular pattern. Thus, we hypothesized the following: \"In the YR estuary with artificial and intermittent freshwater inflow, the variation in phytoplankton size structure is significantly influenced by environmental changes induced by freshwater inflow, including changes in salinity, nutrients, stratification, and turbulence, and these influences are greater than those of seasonal changes such as radiation intensity and water temperature. The responses to such environmental changes vary according to the phytoplankton size structure.\"  
To investigate the phytoplankton variations and the regulatory factors in the altered and complex aquatic system of the YR estuary, an artificial neural network (ANN) was applied. An ANN is a model based on the mathematical description of the brain's information-processing mechanisms that underlie human cognition and determination. The model is, thus, widely used in describing intricately related problems through learning [21; 22; 23; 24; 25]. Furthermore, ANNs are advantageous in that no mathematical relationship or assumption of data distribution is required; response time is relatively brief, and the prediction power improves with data accumulation. Ultimately, ANNs are a useful model in cases where identifying main regulatory factors is difficult due to the intricate connections among the biological, physical, and chemical factors. This describes the situation of phytoplankton variations in an altered estuary. Recently, ANNs have been utilized to investigate phytoplankton dynamics [26; 27]; however, there are only a few published studies of variations in algal size structure. Understanding the mechanism that drives phytoplankton size structure variations is challenging in the case of an altered estuary with artificial and irregular changes in the environment. Accordingly, a study based on long-term (~10 year) data may contribute to the understanding of algal blooms as well as the aquatic ecosystem in an altered estuary, such as the YR estuary that experiences severe anthropogenic changes in the environment. Thus,in the present study, an ANN model based on long-term data collected from 2008 to 2018 was employed to describe the size-dependent changes in the phytoplankton community structure and the complex and simultaneous correlations among environmental factors. Furthermore, the main environmental factors influencing phytoplankton size structure variations were identified using this novel model.