## 5 Conclusions  
This study created a sequential way of systematically and efficiently managing the huge amount of data retrieved from SPEs and rain gauge observations. This study investigated the optimal way to effectively integrate the separate inputs and merge them into a single dataset. This could potentially be changing the way researchers conduct their conventional research. The motivation behind this method is to enable researchers to quickly process and visualize large datasets to better analyze the dataset by using a sequential of programs such as Python, pHp, SQL, etc. Rainfall prediction can be easily explored and trained with these programs. In the present study, the LSTM is also proposed in enhancing the accuracy of the SPEs. This model is a great neural network model that has the advantage of processing data with time series elements. Based on the outputs, the LSTM with the ADAM optimizer showed the capability of improving the accuracy of SPEs. The variations observed across different grids and time periods provides valuable ground for further investigation into the factors influencing the effectiveness of the model. As the degree of improvement was not at the same level for the whole study area, it is recommended to perform a different model for those regions with a poorer performance with the different auxiliary factors included. Deep learning model training for rainfall data requires a systematic methodology as it is very time-consuming and could be experimentally oriented. Different comparative data scales such as weekly and monthly precipitation scores can be the consideration for future studies using different machine learning approaches. This comprehensive and scalable data processing and analysis pipeline used for deep learning modeling can be applicable for other areas such as flood forecasting and modeling, agricultural harvesting forecast, soil erosion, and land slide prediction in any region that have such domain challenges. Future research should also consider the use of LSTM regularization techniques with the identification of different penalty terms for the loss function with optimized hyperparameter sets. Dropout and early stopping could prevent overfitting and improve the generalization ability of LSTM models.  
Conceptualization, S.H.L., M.M., F.Y.T., E.Z.X.S. and S.C.T.; methodology, S.C.T., S.H.L. and M.M.; software, S.C.T.; Validation, S.H.L. and M.M.; formal analysis, E.Z.X.S. and S.C.T.; investigation, F.Y.T.; resources, S.H.L.; data curation, M.M.; writing--original draft preparation, S.C.T.; writing--review and editing, E.Z.X.S., S.H.L., M.M. and F.Y.T.; visualization, S.C.T.; supervision, S.H.L., M.M. and F.Y.T.; project administration, S.C.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets analyzed during the current study are available from the corresponding authors upon reasonable request.  
The authors would like to thank the Malaysian Department of Irrigation and Drainage (DID) for providing the daily precipitation data as well as the developers of the satellite estimations for providing the downloadable data.  
The authors declare they have no competing interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IMERG & Integrated Multi-Satellite Retrievals for GPM \\ LSTM & Long Short-Term Memory \\ ADAM & Adaptive Moment Estimation \\ MAE & Mean Square Error \\ RMSE & Root-Mean-Square Error \\ KGE & Kling-Gupta Efficiency \\ SQL & Structured Query Language \\ PHP & Hypertext Pre-processor \\ ML & Machine Learning \\ DL & Deep Learning \\ SPE & Satellite Precipitation Estimation \\ RNN & Recurrent Neural Network \\ CSV & Comma Separated Values \\ DID & Department of Irrigation and Drainage \\ EXE & Executable \\ HTML & Hypertext Markup Language \\ \end{tabular}