## 1 Introduction  
Natural disasters occur more and more frequently with global warming. Abnormal natural disasters usually occur suddenly, such as rainstorms, heavy fog, and earthquakes. These natural disasters have the characteristics of suddenness, short duration, and serious impact, which bring difficulties to forecast and control. If we can predict them in advance and prepare for prevention, we can minimize loss and ensure the safety of human beings and other creatures, which is of great significance. However, most of the existing environment forecast models use the total average error to evaluate prediction results, and those models will have great error fluctuations when natural disasters occur suddenly. Therefore, when predicting the occurrence of natural disasters, we should not only know the prediction results of the model, but also grasp the fluctuation of model errors, which can provide a reference for prediction reliability. The excessive growth of algae is known as an algal bloom. Harmful algal blooms (HABs) are one of the most serious water pollution problems in eutrophic waters. Suitable conditions such as increased nutrients, water temperature, salinity, and low circulation are all responsible for algal blooms. HABs can lead to severe economic and ecological impacts in coastal areas and threaten marine life and human health. Red tide is a well-known form of algal bloom. It is a kind of ecological abnormal phenomenon that certain tiny marine plankton in the ocean propagate explosively or gather in high density in a short period of time, causing the color of the sea water to change. In the past few decades, there has been an increasing trend in the occurrence of red tides throughout the world, resulting in a great loss of fishery economy [1]. Since the loss caused by red tide is huge, if the instability of red tide prediction can be forecasted in advance, the loss caused by it should be reduced greatly. Some scholars have also discussed the relationship between meteorological factors such as temperature, pressure, rainfall, light, and red tides. Previous studies have shown that the change of Chl-a is the most direct indicator of algae growth in seawater, which is also an integrated indicator of phytoplankton biomass [2]. Most methods of red tide prevention and control can be divided into two categories at present. The first type is based on red tide monitoring. [PERSON] et al. proposed fBm based Lagrangian particle-tracking model to predict the trends and the main features of red tide drifting successfully [3]. [PERSON] used a machine learning based spatio-temporal data mining approach for detection of harmful algal blooms in the gulf of Mexico [4]. The second type is mainly to predict the occurrence of red tides. Machine learning methods were used to predict the occurrence of red tides by some scholars [5]. [PERSON] et al. proposed a new empirical switching algorithm to evaluate the root mean squared error (RMSE) for MODIS (MODerate resolution Imaging Spectroradiometer) Chl-a [6]. Among these methods, Artificial Neural Network (ANN) is the most widely used Machine Learning (ML) method to predict algal blooms [7; 8], especially the back propagation (BP) neural network [9]. However, due to the red tide being an explosive process rather than a gradual process, most of the current red tide prediction methods have the problems of inaccuracy and error fluctuations. When red tide occurs, the value of Chl-a in the seawater rises suddenly. Existing red tide forecasting methods cannot predict the rapid change of Chl-a value when red tide breaks out, and do not consider the fluctuation of model errors to provide a reference for forecasting reliability. Recently, Long Short-Term Memory (LSTM) [10] and attention mechanisms have been proposed. LSTM is one kind of the recurrent neural network which can handle long-term information dependency, and it has received a great amount of attention due to its flexibility in capturing nonlinear relationships.