## 3 Experiment and Results
### Experiment-I  
We compare our model with the following three baselines:  
1. BP: Back-propagation neural network (BP) [9] is the most widely used ML method to predict harmful algal blooms. A lot of studies have shown the efficiency of it.
2. Seq2 seq: It uses an RNN to encode the input sequences into a feature representation and another RNN to make predictions iteratively [19].
3. Attention RNN: Attention RNN is the attention-based encoder decoder network that employs an attention mechanism to select parts of hidden states across all the time steps [12].  
In this section, we first evaluate the predictive performance of DA-RNN for Chl-a. To be fair, we presented the best performance of each method under different parameter settings in Table 4. We also set different time intervals for comparison, which are 6, 12, 18, and 24. The time series prediction results of DA-RNN and baseline methods over the dataset are shown in Table 4, we can clearly observe that DA-RNN achieves the best performance both on RMSE and MAE when time intervals are 6 and 12 and 18. When the prediction interval is 6, BP performs worst both on RMSE and MAE. When prediction intervals are 12 and 18, Seq2 seq always performs the worst both on RMSE and MAE. However, DA-RNN does not always perform the best, when prediction interval is 24, the MAE of DA-RNN is slightly higher (0.848 vs 0.831) than BP, but the RMSE of DA-RNN still performs best,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Prediction Intervals**} \\ \cline{2-6}  & \multicolumn{1}{c}{**m = p**} & \multicolumn{1}{c}{**6**} & **12** & **18** & **24** \\ \hline \multirow{2}{*}{**32**} & RMSE & 1.265 & 1.229 & 1.233 & 1.29 \\ \cline{2-6}  & MAE & 0.795 & 0.819 & 0.836 & 0.855 \\ \hline \multirow{2}{*}{**64**} & RMSE & 1.269 & 1.201 & 1.205 & 1.215 \\ \cline{2-6}  & MAE & 0.79 & 0.778 & 0.814 & 0.848 \\ \hline \multirow{2}{*}{**128**} & RMSE & 1.286 & 1.255 & 1.321 & 1.322 \\ \cline{2-6}  & MAE & 0.819 & 0.909 & 0.939 & 0.904 \\ \hline \multirow{2}{*}{**256**} & RMSE & 1.389 & 1.285 & 1.345 & 1.404 \\ \cline{2-6}  & MAE & 0.834 & 1.017 & 1.104 & 1.112 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter determination.  
indicating that DA-RNN has more advantages in the long-term predictions than Seq2 seq and Attention LSTM and BP.  
Figure 3 shows the comparison of Chl-a prediction results using four different methods when the time interval is 12. In order to see the comparison results more clearly, we only show the 300 prediction results. The \(X\) axis represents the time interval and the \(Y\) axis represents the Chl-a value. The dark blue line represents the true value. The orange line represents the prediction results of DA-RNN network. Gray line represents the prediction results of Attention LSTM. The yellow line represents the prediction results of BP network, and the light blue line represents the prediction results of Seq2 seq network. As shown in Figure 3, the prediction results of DA-RNN are closer to real values than the other three methods. It also indicates that the DA-RNN can be a good choice for dealing with the Chl-a prediction problem.