Next Article in Journal
Understanding How Image Quality Affects Transformer Neural Networks
Next Article in Special Issue
Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes
Previous Article in Journal
Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Review of Rainfall Estimation in Indonesia: Data Sources, Techniques, and Methods

Department of Physics, FMIPA Universitas Indonesia, Depok 16424, Indonesia
*
Author to whom correspondence should be addressed.
Signals 2024, 5(3), 542-561; https://doi.org/10.3390/signals5030030
Submission received: 6 June 2024 / Revised: 31 July 2024 / Accepted: 9 August 2024 / Published: 16 August 2024
(This article belongs to the Special Issue Rainfall Estimation Using Signals)

Abstract

Rainfall information with high spatial and temporal resolution are essential in various fields. Heavy rainfall in a short period can cause problems and disasters that result in loss of life and damage to property. Conversely, the absence of rain for an extended period can also have negative social and economic impacts. Data accuracy, wide spatial coverage, and high temporal resolution are challenges in obtaining rainfall information in Indonesia. This article presents information on data sources and methods for measuring rainfall and reviews the latest research regarding statistical algorithms and machine learning to estimate rainfall in Indonesia. Rainfall information in Indonesia was obtained from several sources. Firstly, the method of direct rainfall measurement conducted with both manual and automatic rain gauges was reviewed; however, this data source provided minimal results, with uneven spatial density. Secondly, the application of remote sensing estimation using both radar and weather satellites was reviewed. The estimated rainfall results obtained using remote sensing showed more comprehensive spatial coverage and higher temporal resolution. Finally, we reviewed rainfall products obtained from model calculations, using both statistical and machine learning by integrating measurement and remote sensing data. The results of the review demonstrated that rainfall estimation products applied in remote sensing using machine learning models have the potential to produce more accurate spatial and temporal data. However, the validation of rainfall data from direct measurements is required first. This research’s contribution can provide practitioners and researchers in Indonesia and the surrounding region with information on problems, challenges, and recommendations for optimizing rainfall measurement products using appropriate adaptive technology.
Keywords: rainfall; rain gauge; radar; satellite; machine learning rainfall; rain gauge; radar; satellite; machine learning

Share and Cite

MDPI and ACS Style

Putra, M.; Rosid, M.S.; Handoko, D. A Review of Rainfall Estimation in Indonesia: Data Sources, Techniques, and Methods. Signals 2024, 5, 542-561. https://doi.org/10.3390/signals5030030

AMA Style

Putra M, Rosid MS, Handoko D. A Review of Rainfall Estimation in Indonesia: Data Sources, Techniques, and Methods. Signals. 2024; 5(3):542-561. https://doi.org/10.3390/signals5030030

Chicago/Turabian Style

Putra, Maulana, Mohammad Syamsu Rosid, and Djati Handoko. 2024. "A Review of Rainfall Estimation in Indonesia: Data Sources, Techniques, and Methods" Signals 5, no. 3: 542-561. https://doi.org/10.3390/signals5030030

APA Style

Putra, M., Rosid, M. S., & Handoko, D. (2024). A Review of Rainfall Estimation in Indonesia: Data Sources, Techniques, and Methods. Signals, 5(3), 542-561. https://doi.org/10.3390/signals5030030

Article Metrics

Back to TopTop