###### Abstract
Long short-term memory (LSTM), an RNN architecture used in the field of DL [38; 39], is one of the most popular methods to be applied to improving SPEs [40; 41; 42; 43]. It was proposed by [PERSON] and [PERSON] in 1997 [43]. In a recent study conducted by [PERSON] et al. (2023), various deep learning methodologies were examined to assess their effectiveness in predicting the streamflow for the Muda River Basin which is located in the northern part of Peninsula Malaysia. The findings indicate that LSTM outperformed other approaches, demonstrating superior performance [44]. Our study attempts to improve the SPEs by merging the satellite rainfall and gauge precipitation using the LSTM application with the adaptive moment estimation (ADAM) optimizer. ADAM optimizer has shown good results in deep learning modeling [45]. Research performed in China to test the prediction capabilities of the weighted mean temperature, LSTM outperformed traditional RNN [46]. Generally, LSTM utilizes certain types of artificial memory processes that can help more effectively imitate human thought. LSTM works in the manner that, with discrete time steps \(t\) = 0, 1, 2, 3, 4, 5, 6, \(\ldots\), units' activation will be updated (forward pass) followed by all weights' error signals computation (backward pass) [47]. Previous research has shown that LSTM is able to address the vanishing gradient problem when learning long-term dependencies that arise in traditional RNNs [48]. It has been proven that this method is better than the traditional RNN and is capable of learning long-term dependencies much faster [40; 43; 49; 50]. The LSTM used in our study is one of the variants in LSTM architecture that adds \"peephole connections\" to let the gate layers look at the cell state to learn the precise timing of the outputs [47]. Managing large volumes of data poses a significant challenge for researchers in various fields. To address this issue, we propose a sequential program that aims to fill the gap and provide an effective solution for handling massive amounts of data. In this paper, we present the design and implementation of the program, along with its performance evaluation, comparison, and enhancement of the SPE. A systematic methodology is introduced to perform the data extraction and comparison of the satellite rainfall estimations with rain gauge observations with a sequence of programs using different technologies, such as Python for statistical analysis and modeling, pHp for the interactive visualizations of the results, MariaDB for relational database storage with SQL for data pre-processing, and Shell Script for data extraction automation. According to a recent study conducted by [PERSON] et al. (2021) concerning the data collection survey for machine learning, the importance of data cleaning due to the prevalent presence of data noise was highlighted [51]. It is essential to identify and remove erroneous data points from both satellite rainfall estimations and rain gauge observations [52; 53] to enhance the quality of the data. SQL programs are used to filter the erroneous dataset. Data visualization in the form of a scatter plot and time series graph could help in effectively comparing the performance of the dataset [54]. In this paper, we propose a novel approach to develop a comprehensive and scalable data processing and analysis pipeline for the deep learning modeling of rainfall data that can be applicable and adaptable to different datasets and regions. The proposed approach can be effectively applied to various datasets and regions, as long as comparable resources such as satellite rainfall and rain gauge data are accessible and the procedures outlined in this investigation are followed. Additionally, the same approach can be applied to forecasting and modeling in other fields of study. Researchers have been using rain gauge as the reference to enhance the satellite rainfall data using machine learning.