Machine Learning-Based Spatial Downscaling on Precipitation Satellite Data in Riau Province, Indonesia
Main Article Content
Abstract
Precipitation is a condensation process on water from the atmosphere that causes rain. Rain has positive and negative impacts on society, especially because of global warming. Estimating precipitation at specific locations is needed t o mitigate the negative consequences. Utilizing satellite data is the best way to estimate precipitation, but it has a coarse resolution. Therefore, a study was conducted on spatial downscaling for increasing spatial resolution of precipitation data from the tropical rainfall measuring mission satellite using machine learning in Riau Province, Indonesia. We compared machine learning models namelydecision trees, multiple linear regression, support vector machines, and random forest to downscale the data. We used variables like normalized difference vegetation index, digital elevation model, and land cover as the input for the model. Also, we validated the result with the measurement of the rain gauge station at Riau Province Indonesia. Based on the
study, we found that the decision tree is the best model to downscale the precipitation data in Riau Province with the mean square error value of 0.00048 and the R2 value of 0.67107. We found that the digital elevation model is the most important variable in downscaling the precipitation data.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.