## 1 Introduction  
Water is essential for human as well as natural systems and is the most critical resource for ecosystem stability and sustainable social development [1]. In particular, surface water is considered to be one of the most important ecological factors that play a role in balancing the water cycle. However, with the continuous development of industrial civilization, climate change, inappropriate human land resource utilization practices, and gross negligence in water quality management have led to further increase in surface water pollution [2, 3]. Therefore, the assessment and estimation of river water quality level is a great concern today. An accurate estimation and assessment of river water quality is the basis of water environment management, which is important for the effective protection of water environment. To make the relevant staff in water environment assessment with practical and quantitative information, [PERSON] et al. [4] proposed WQI, the water quality index, which is calculated based on various physicochemical parameters of water (such as turbidity (TUR), total nitrogen (TN), PH and other pollutant concentrations), aiming to effectively assess the overall quality of water sources, and has been extensively used in the work related to the field of water quality. However, on the basis of its large calculation volume, the process of obtaining WQI requires a lot of time and effort of professional staff [3]. In addition, the different calculation methods of WQI also lead to an increase in workload. Therefore, improving the efficiency and estimation ability of WQI calculation exists a high practical application value [5]. At this stage of research, the comprehensive water quality index WQI prediction by time-series prediction model can effectively assess the water quality and on this basis to achieve the early detection of pollution, timely prevention, and control of water sources [6]. The good performance of deep learning in the case of large data size and the more powerful information capture ability of covariate prediction methods have attracted the attention of researchers. [PERSON] et al. [7] used Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) for water quality components during the study of Karoon River water quality, and pointed out that all the applied models have suitable performances in water quality prediction, among which MLP has slightly higher prediction accuracy in all contexts, but it is not difficult to find in the final results that these methods, although improved over previous ones, are still deficient in capturing long-term dependence. Later, [PERSON] et al. [8] created a new deep learning method by using long short-term memory (LSTM) to conduct a study in the Nakong River basin in Korea, simulating a variety of parameter variables affecting water quality conditions in the process. LSTM itself solves the problems of gradient disappearance and gradient explosion that exist in RNN-like methods when the network is deepened [9]. Such improvements significantly improve the utilization of data and the accuracy of predictions of regional water quality. However, it is still weak in capturing long-term dependence [10], and its mastery of auxiliary variables as one of the models for covariate forecasting still needs to be improved. With the continuous development in the field of deep learning coupled with time series prediction, Long Sequence Time-Series Forecasting (LSTF) has gradually started to be noticed by researchers. Compared with short time-series prediction, LSTF is closer to realistic scenarios [11], it can effectively capture the more accurate long-range correlation coupling between output and input, and this kind of prediction can provide us with timely warning of pollution criticality [12] and thus maintain the ecological environment. balance.