## 4 Discussion
However, combining these two data sources requires consideration of seasonal differences, types of rain events, topography, and the distance between the rain gauge and radar [105, 106, 150]. Mountain and urban areas in Indonesia should utilize X-band radar because it has a shorter wavelength, which allows precipitation detection with higher spatial resolution. It is crucial in urban areas that require detailed mapping. In addition, the X-band radar is more effective in overcoming beam blockage problems in mountainous areas because its working range allows more flexible placement and better avoidance of physical obstacles. C-band and S-band radars are suitable for placement on flatlands or coasts with wide ocean coverage. Longer ranges are critical for detecting tropical storms that develop over the ocean and move toward land. In addition, flatland and coastal areas usually do not have many physical obstacles, such as mountains or tall buildings, allowing for wider radar coverage. Statistical method validation can be carried out using satellite data with a rain gauge or weather radar. In data validation using rain gauges, it is important to consider the number and distribution of rain gauges because these factors can significantly influence the estimation results. Meanwhile, machine learning approaches have been applied to estimate rainfall in Indonesia using algorithms such as multi-layer perceptron (MLP), decision tree, random forest, adaptive boosting, gradient boosting, and XGBoost. In the course of our research regarding rainfall estimation, especially in Indonesia, we determined that the optimality of machine learning algorithms depends on the characteristics of the data used [130]. We have reviewed the rapid development of the application of machine learning around the world, including rainfall estimation [151, 152, 153]. It shows great potential for application in Indonesia as a tropical country. For example, research using over-parameterized neural network methods with remote sensing data can describe the spatial patterns of each type of rain in tropical regions [154]. Another study developed a monthly rainfall estimation model using monthly rainfall data for 54 years by combining optimization algorithms, including artificial bee colony, particle swarm optimization, imperialism competitive algorithm, and artificial neural network (ANN). The research results can help the climate planning process in tropical regions [155]. Furthermore, new methods combining data from various sources such as rain gauges, radar, and satellites to estimate rainfall have been introduced for areas that are difficult to reach by traditional measuring instruments [151]. Some of these approaches would be appropriate if applied in remote and maritime areas, which are often challenges in Indonesia. The computing technology of machine learning enables the quick and efficient processing of more significant data. Machine learning models can also capture complex non-linear patterns and relationships to produce accurate and adaptive rainfall estimates. These developments and successes should be applied to several case studies in Indonesia. Several studies show that in general, the trend of using machine learning is starting to be implemented in the rainfall estimation process in Indonesia. These studies can be supported by the availability of C-band and X-band weather radar networks, as well as information from satellites that can be accessed. As data sources increase and the resolution of rainfall estimates increases, the challenge for the future is to prepare the capacity of the processing system so that the computing process can run optimally [156, 157]. This is also in line with improvements in hyperparameter tuning and feature selection in training data.