# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 4 Conclusions  
The effectiveness of flood management and disaster preparedness is in tandem with the ability to accurately forecast the immediate condition of streamflow in the catchment area. This study intended to develop the best deep learning model for the SMART control center in managing the river flow through streamflow forecasting. The aim was to create a novel approach in using rates of change for model development and introduce new metaheuristic algorithms with LSTM hybrid models to enhance the performance results. This study employed LSTM models to develop and train historical data at the Ampang River and Klang River. The task is to forecast river streamflow with simulation, 30 min ahead, 1 h ahead and rates-of-change models. In order to ascertain the best performance that can be achieved, three steps of the improvement process were introduced. Step 1 is where the comparison of ANN and LSTM models is performed. The best results come from the LSTM model with regression 0.9055, MSE 17,8532, MAE 1.4365 m\({}^{3}\)/s, NSE 0.8190 and RMSE 5.3695 m\({}^{3}\)/s. ANN yielded weaker results, and therefore LSTM model is the center of this research. Step 2 introduces rates of change and performs multi-step ahead streamflow forecasting. The best result comes from the \(\delta Q\) model with performance values of \(R\) (training) = 0.9545, \(R\) (testing) = 0.9214, MSE (training) = 8.9746, MSE (testing) = 15.6981, MAE (training) = 0.5434 m\({}^{3}\)/s, MAE (testing) = 0.8108 m\({}^{3}\)/s, NSE = 0.9090 and RMSE = 2.9958 m\({}^{3}\)/s. The finding reveals that a shorter forecasting time yields better performance results. The second finding shows \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **MAPE** & **MAE m\({}^{3}\)/s** & **Max. Error** \\ \hline Bat-LSTM algorithm & & & \\
0 min & 26.41\% & 59.6951 & 64.71\% \\
30 min & 24.03\% & 55.7999 & 64.02\% \\
1 h & 37.63\% & 80.4564 & 83.81\% \\ \(\delta Q\) & 6.33\% & I2.2865 & 97.70\% \\ \hline Firefly-LSTM algorithm & & & \\
0 min & 15.11\% & 31.7240 & 56.92\% \\
30 min & 11.33\% & 22.8895 & 56.52\% \\
1 h & 27.55\% & 55.1539 & 86.05\% \\ \(\delta Q\) & 7.40\% & 14.6589 & 97.71\% \\ \hline Jaya-LSTM algorithm & & & \\
0 min & 17.29\% & 37.1065 & 61.75\% \\
30 min & 11.08\% & 24.7873 & 53.09\% \\
1 h & 16.98\% & 35.3379 & 78.41\% \\ \(\delta Q\) & 6.22\% & 12.6687 & 97.70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of LSTM-optimization algorithm models for streamflow forecasting. that applying new rate changes in model development has significantly improved the model results. The last step of the experiment is to introduce new hybrid models between optimization and LSTM algorithms. The bat algorithm, firefly algorithm and Jaya algorithm were selected for this study. From the results, all hybrid models demonstrate better outcomes. Therefore, the third finding shows that metaheuristic algorithms play a role in model improvement. Under this study, it is also noticeable that the selection of an optimization algorithm does not significantly affect performance. SQ model for the bat algorithm with LSTM hybrid model yielded the best results with \(R\) (training) = 0.9757, \(R\) (testing) = 0.9046, MSE (training) = 4.7187, MSE (testing) = 19.8966, MAE (training) = 0.4672 m\({}^{3}\)/s, MAE (testing) = 0.8565 m\({}^{3}\)/s, NSE = 0.9514 and RMSE = 2.1723 m\({}^{3}\)/s. Findings from this study are beneficial to improving the deep learning process so that the performance can yield better results with higher precision. This knowledge also helps elevate a new approach to flood mitigation operations. This study is significant as it has presented several new steps to improve the learning process leading to a better relationship between the input and output data. The current study is limited to a small catchment area and several optimization models. The results may differ for bigger catchments and with more optimization models. In order to further improve the experiment, it is suggested to try reinforcement learning for future studies.